# Pinin — Full documentation text

> This is the full-text version of Pinin's documentation for AI photo restoration, colorization, and enhancement. It pairs with llms.txt, which links every page. Each article below is reproduced verbatim from https://pinin.ai/docs.

## How to Restore Photos for Memorials, Funerals & Tributes

Source: https://pinin.ai/docs/how-to-restore-photos-for-memorials

# How to Restore Photos for Memorials, Funerals & Tributes When you lose someone, the photos you have of them become suddenly, painfully important. A faded or damaged picture of a loved one can be restored — often in minutes — so it can be used at a service, in a tribute video, or as a keepsake for the family. This guide is written with care. It covers the practical steps for restoring memorial photos quickly and respectfully, what to expect, and how to get the best result when time is short. > **A note on timing:** if you need a photo for an upcoming service, AI restoration is the fastest option — a single photo takes about 12 seconds to process, and your first restoration is free. You don't need to be a designer or buy software. ## What can be done with memorial photos | Need | What AI restoration can do | | --- | --- | | Service photo (print or slideshow) | Repair damage, restore color, upscale for projection | | Tribute video | Enhance and upscale the photo to match video resolution | | Obituary image | Clean scratches and fading for a dignified print | | Keepsake / memorial card | Restore and colorize for a beautiful memento | | Memorial website / social post | Clean, sharp, shareable image | ## Step-by-step: restore a memorial photo 1. **Choose the best original** — the sharpest, most complete photo of your loved one. Multiple photos let you pick the one with the most intact face. 2. **Digitize it** — scan at 600 DPI, or photograph it flat in even, indirect light (no flash). If the original is damaged, handle it minimally. 3. **Upload to Pinin** — open the [AI photo restoration tool](/photo-restoration). Your first restoration is free. 4. **Run the restoration** — the AI repairs scratches, fading, and blur in about 12 seconds. For black-and-white originals, add a [colorization pass](/colorization). 5. **Enhance and upscale** — one [enhancement pass](/ai-enhancement) makes the photo crisp for printing or projection. 6. **Download and share with family** — send the restored photo to family members; often several people are looking for a good image of the same person. ## Restoring a photo of someone who passed long ago If you're restoring a photo of a parent, grandparent, or ancestor for a memorial, the same workflow applies — and the [ancestor restoration guide](/docs/how-to-restore-ancestor-photos) has detailed advice for fragile originals and very old formats. ## FAQ **How fast can I get a restored photo?** With Pinin, processing takes about 12 seconds per photo. A single restored photo is usually ready in under a minute from upload. **Is it respectful to "AI-restore" a memorial photo?** Yes — restoration recovers what the original photo showed. Families overwhelmingly value seeing a loved one's face clear and whole. You stay in control: you can choose how much enhancement to apply. **Can I use the restored photo in a slideshow or video?** Yes. Use the enhancement engine to upscale the photo to the resolution you need, then drop it into your slideshow or video editor. **Is the first photo really free?** Yes. Your first restoration on Pinin is free, no credit card required — no reason to hesitate when time is short. Restore a photo of someone you love — [start now](/photo-restoration).


## How to Repair Torn Photos with AI — Fix Tears and Rips Without Photoshop

Source: https://pinin.ai/docs/how-to-repair-torn-photos

# How to Repair Torn Photos with AI — Fix Tears and Rips Without Photoshop A torn photo is one of the most heartbreaking kinds of damage — the physical print is broken, and every fold or missing piece feels like part of the memory is gone too. The good news: **AI photo restoration can repair most tears and rips automatically**, and the results are often indistinguishable from an untouched print. This guide covers what an AI repair actually does, which tears it can and can't fix, and the exact steps to get the best possible result. ## Why torn photos are fixable A tear is a **localized** defect: the pixels on both sides of the rip are intact, they're just displaced. The AI's job is to: - Detect the tear boundary and the small wedge of missing pixels along it - Rebuild the missing strip by inferring what the surrounding texture, lines, and colors imply - Blend the repair so no seam or blur halo remains Because the surrounding image gives the model strong clues, tears are among the **highest-success-rate** repairs — far easier than filling a completely missing face. > **Good to know:** the bigger the missing area, the harder the repair. A clean rip with both halves present usually restores perfectly. A missing corner of a print needs more inference, and a photo with an entire face torn away may need manual restoration — see the manual option below. ## What counts as a "torn" photo | Damage | AI repair quality | Notes | | --- | --- | --- | | Single clean rip, both halves present | Excellent | The classic "torn in half" photo — best case | | Fold crease with a small tear along it | Excellent | Often fully invisible after repair | | Missing corner / chunk | Good | AI fills the gap; complex backgrounds may show faint artifacts | | Multiple rips with missing pieces | Fair | Run repair, then retouch remaining gaps | | Entire subject torn away (face/body) | Poor | Needs manual restoration or a donor photo | ## Step-by-step: repair a torn photo with Pinin 1. **Scan both pieces** — place the torn halves together (don't tape them; just align them) and scan at 600 DPI, or photograph flat with even light. 2. **Upload to Pinin** — go to the [AI photo restoration tool](/photo-restoration) and upload the scan. Your first restoration is free. 3. **Choose the repair mode** — select the restoration engine that handles damage. Pinin repairs tears, scratches, and fading in one pass. 4. **Let the AI rebuild the tear** — processing takes roughly 12 seconds. The model reconstructs the missing strip from the surrounding texture. 5. **Download and inspect** — zoom in on the repaired seam. If a faint line remains, run the enhancement engine to blend it further. ## Tips for the best repair - **Scan before you repair.** A high-resolution source gives the AI more real pixels to work with. 600 DPI is the sweet spot for prints. - **Align the pieces carefully.** The closer the torn edges are to their original position, the less the AI has to invent. - **Don't worry about dust on the glass.** The repair pass usually removes dust and specks along with the tear. - **Use enhancement afterward.** After the tear is closed, one enhancement pass sharpens the rebuilt area so it matches the rest of the photo. ## When manual restoration is the right call If a large part of the subject is missing — say, half of a face or an entire person — the AI has nothing to infer from. In that case, Pinin's [manual restoration service](/manual-restoration) pairs you with a human artist who can rebuild the missing area convincingly. It costs more and takes longer, but for irreplaceable photos it's worth it. ## FAQ **Can AI repair a photo torn into many pieces?** Yes, if you can align the main pieces. The AI treats the assembled scan as one image and rebuilds the seams. Tiny fragments add little information, so focus on the two or three largest pieces. **Will the repair look like a patch?** With a clean scan and a single tear, no. The model blends the rebuilt strip with the surrounding texture. On very textured backgrounds (grass, fabric, hair) you may see a faint artifact — a second enhancement pass usually removes it. **Is repairing a torn photo free?** Your first restoration on Pinin is free, no credit card required. After that, each restoration costs one credit. Put it into practice — [repair a torn photo now](/photo-restoration) and see the seam disappear.


## How to Remove Scratches from Old Photos — AI Scratch Removal

Source: https://pinin.ai/docs/how-to-remove-scratches-from-old-photos

# How to Remove Scratches from Old Photos — AI Scratch Removal Scratches are the most common damage on old photos — they come from storage in albums, glass frames, and decades of handling. The good news is they're also the **easiest damage for AI to remove**. A scratch is a thin line of missing or damaged pixels; the model fills it from the intact pixels on either side, and the result is usually perfect. This guide explains how scratch removal works, which scratches are fixable, and the fastest way to clean up a whole album. ## Why AI is great at removing scratches A scratch is **narrow and linear** — exactly what a restoration model is trained to recognize. Unlike a tear or a faded area, the surrounding pixels give the AI everything it needs to reconstruct the line: - **Detection** — the model finds the scratch as a bright or dark line that doesn't match local texture - **Inpainting** — it fills the line using the color, brightness, and texture of the pixels on both sides - **Blending** — it softens the edges so no repair seam is visible Because scratches are thin, the repair rarely hallucinates detail — it just continues the existing texture across the line. > **Good to know:** white scratches (emulsion damage) and black scratches (dirt embedded in the print) are both fixable. Deep gouges where the print is physically indented may leave a faint shadow — enhancement usually finishes the job. ## What types of scratches can be removed | Damage | AI quality | Notes | | --- | --- | --- | | Fine surface scratches | Excellent | Invisible after one pass | | Album-sleeve scratches (parallel lines) | Excellent | The model removes the whole family of lines | | Dust specks and lint | Excellent | Often removed without being asked | | Mold lines and foxing | Good | May need two passes on heavy infestations | | Deep gouges | Fair | A faint trace can remain; enhancement helps | ## Step-by-step: remove scratches with Pinin 1. **Scan or photograph the print** — 600 DPI for prints; hold the phone parallel and avoid flash glare. 2. **Upload to Pinin** — open the [AI photo restoration tool](/photo-restoration) and upload your image. The first restoration is free. 3. **Run the repair engine** — Pinin's restoration handles scratches, dust, and mold lines in one pass. 4. **Zoom in and check** — the scratch lines should be gone. If heavy damage remains, run a second pass. 5. **Enhance for extra clean** — the [enhancement engine](/ai-enhancement) sharpens the whole image and removes residual noise. ## Batch-removing scratches from a whole album If you have a box of scratched photos, don't run them one at a time — use [batch restoration](/batch-processing). Upload up to 100 images at once; Pinin applies the same restoration to every photo with consistent results. Each photo costs one credit, and the first is free. ## FAQ **Can AI remove scratches from a photo without damaging the image?** Yes. Modern restoration models only alter the scratch lines and leave the rest of the image untouched — no blur, no color shift, no "AI look." **How many passes does a scratched photo need?** Most need one. Heavily scratched or moldy photos may need a second repair pass, and a final enhancement pass to sharpen the result. **Will scratch removal also fix fading or color issues?** Not by itself — it focuses on surface damage. For faded or yellowed photos, run the white-balance/color restoration pass as well. See our guide on [fixing faded photos](/docs/how-to-fix-faded-photos). **Is scratch removal free?** Your first restoration on Pinin is free, no card required. Each photo after that costs one credit. Start cleaning up your album — [remove scratches from a photo now](/photo-restoration).


## How to Fix Faded Photos — Restore Color and Contrast with AI

Source: https://pinin.ai/docs/how-to-fix-faded-photos

# How to Fix Faded Photos — Restore Color and Contrast with AI Every old photo fades eventually. Sunlight, heat, humidity, and the natural breakdown of dyes all drain color and contrast from prints over decades. A photo that was once vibrant becomes pale, yellowed, and flat — but the information is often still there, hiding in the pixels. **AI color restoration can bring most of it back.** This guide explains what causes fading, which faded photos recover best, and how to fix them with Pinin in seconds. ## Why faded photos are recoverable Fading is a **gradual, global** change: the dyes have lost intensity, but the relationships between colors are usually preserved. The AI restoration model: - **Analyzes the color cast** — detects the yellow/brown shift caused by age - **Restores white balance** — brings whites back to white and removes the sepia cast - **Rebuilds contrast** — stretches the tonal range so shadows deepen and highlights brighten - **Re-saturates** — revives the faded color channels without pushing them into an unnatural "HDR" look The result looks like the photo did when it was printed — not like a filter, but like the original. > **Key insight:** a photo that looks "beyond saving" to the eye is often perfectly recoverable. The eye adapts to the cast; the model doesn't. If you can still make out the subjects, the restoration has strong material to work with. ## What types of fading can be fixed | Condition | AI quality | Notes | | --- | --- | --- | | Slight yellowing / sepia cast | Excellent | One pass — whites return to white | | Overall flatness (low contrast) | Excellent | Contrast restored naturally | | Moderate color fade | Excellent | Saturation and color balance recovered | | Severe fade (nearly white image) | Good | Detail is recovered but some color may be unrecoverable | | Chemical stains / foxing | Good | Repair engine handles the stains separately | ## Step-by-step: fix a faded photo with Pinin 1. **Scan the print at 600 DPI** — more real detail means a better color restoration. 2. **Upload to Pinin** — open the [AI photo restoration tool](/photo-restoration). Your first restoration is free. 3. **Run the white balance / color restoration engine** — this is the engine built for faded photos. 4. **Check the result** — compare with the original. Whites should be white, colors natural, contrast restored. 5. **Enhance to finish** — one [enhancement pass](/ai-enhancement) sharpens the restored detail. ## What about black-and-white photos? If your photo is genuinely black and white (not just faded color), the fix is different — you want to **colorize** it, not restore its faded color. Pinin's [colorization engine](/colorization) adds natural, historically plausible color to monochrome photos in seconds. ## FAQ **Can a completely washed-out photo be recovered?** Usually yes, if you can still see the subjects. The model recovers contrast and color from the faint information in the pixels. Extremely faded images may not recover full saturation — the dyes are physically gone. **Will the restored photo look artificial?** No. Pinin's model is trained to restore *natural* color and contrast. It won't oversaturate or add an HDR glow. The result should look like the photo did when it was new. **How is this different from a colorize filter?** A colorize filter applies a uniform tint to everything. AI restoration analyzes each region — skin, sky, clothing, background — and restores them individually. **Is fixing faded photos free?** Your first restoration on Pinin is free, no card required. Each photo after that costs one credit. Bring the color back — [fix a faded photo now](/photo-restoration).


## How to Restore Wedding Photos — Bring Old Wedding Memories Back to Life

Source: https://pinin.ai/docs/how-to-restore-wedding-photos

# How to Restore Wedding Photos — Bring Old Wedding Memories Back to Life Wedding photos are some of the most precious images a family owns — and some of the most damaged. Stored in albums, displayed in frames, and passed down for decades, they arrive in your hands faded, scratched, or stuck to the album page. The good news: **AI photo restoration can bring them back** — the dress white again, the faces sharp, the colors true. This guide covers the common types of wedding-photo damage and how to fix each one. ## The four most common wedding-photo problems | Problem | What it looks like | The fix | | --- | --- | --- | | Faded color | Washed-out dress, yellowed skin tones | [Color/white-balance restoration](/docs/how-to-fix-faded-photos) | | Scratches & creases | White lines across the print | [Scratch removal](/docs/how-to-remove-scratches-from-old-photos) | | Blurry subjects | Soft, out-of-focus faces | [Enhancement / unblur](/ai-enhancement) | | Black & white originals | Monochrome vintage shots | [Colorization](/colorization) | Most wedding photos need **more than one fix** — a 1970s print might be faded *and* scratched. Pinin lets you run multiple engines on the same photo; each pass costs one credit. > **Good to know:** the most valuable part of any wedding photo is the faces. If faces are intact, the restoration will be meaningful even when the background is heavily damaged. ## Step-by-step: restore a wedding photo 1. **Remove it from the album carefully** — if it's stuck to the page, don't pull; a photo-safe adhesive remover or gentle heat helps. If it can't be removed, photograph it in place with even light. 2. **Scan at high resolution** — 600 DPI for standard prints. For large group shots, scan at 600–1200 DPI so faces stay detailed. 3. **Upload to Pinin** — open the [AI photo restoration tool](/photo-restoration). Your first restoration is free. 4. **Run the matching engines** — faded? Run color restoration. Scratched? Run scratch removal. Blurry? Run enhancement. 5. **Download the restored photo** — print-ready, shareable, and safe to back up. ## Restoring a whole wedding album Wedding albums are rarely damaged one photo at a time — they come to you as a box. Use [batch restoration](/batch-processing) to process up to 100 photos at once with consistent results. Set aside the heavily damaged ones for individual attention. ## When to use manual restoration If a key photo — the first dance, the family group — is severely damaged (large missing areas, faces torn away), AI may not be enough. Pinin's [manual restoration service](/manual-restoration) connects you with an artist who can rebuild the missing detail by hand. It's the right choice for the irreplaceable shots. ## FAQ **Can AI restore photos that were damaged by water or mold?** Often yes. Water damage leaves stains and waviness; the repair engine removes the stains and restores the affected areas. Severely warped or disintegrating prints should be professionally scanned first. **Will colorization make a black-and-white wedding photo look accurate?** AI colorization produces historically plausible, natural color — white dresses stay white, skin tones look human. It won't know your grandmother's exact dress color, but the result looks like a period photograph. **How much does it cost to restore a wedding photo?** With Pinin, your first restoration is free. Each photo after that costs one credit — see [how Pinin credits work](/docs/how-pinin-credits-work) for pricing. **Can I print the restored photos?** Yes. Restored photos are high-resolution and print-ready. Many families order prints or albums from the restored files. Restore your family's wedding history — [start now](/photo-restoration).


## How to Restore Ancestor Photos for Family History — Genealogy Restoration Guide

Source: https://pinin.ai/docs/how-to-restore-ancestor-photos

# How to Restore Ancestor Photos for Family History — Genealogy Restoration Guide Every family historian knows the feeling: you find a photo of a great-grandparent — faded, scratched, maybe torn — and it's the only image of that face in existence. **Restoring ancestor photos** is one of the most meaningful projects in genealogy, and AI has made it fast and affordable. This guide walks through the full workflow: digitizing old originals, repairing damage, colorizing 19th-century portraits, and organizing the results for your family tree. ## Why ancestor photos are worth restoring - **They're irreplaceable** — no negative exists, and the print is the only copy - **They connect generations** — a restored face makes family history tangible for younger relatives - **They're scarce** — most people have only a handful of images from before 1900, so each one matters > **Good to know:** restoration isn't "improving" history — it's recovering what the original photo actually showed. The goal is to make the ancestor look like a real person, not an idealized painting. ## The ancestor photo restoration workflow ### 1. Digitize the original carefully Old originals are fragile. Photograph or scan them with minimal handling: - **Tintypes and daguerreotypes** — photograph them angled to avoid reflections from the metal plate; do not scan face-down - **Cabinet cards and cartes de visite** — scan at 600–1200 DPI; the card stock is stable - **Albumen prints** — scan flat and gently; they're sensitive to moisture and pressure ### 2. Repair the damage Once digitized, upload to Pinin and run the repair engine: - Scratches, dust, and mold lines — the [scratch removal](/docs/how-to-remove-scratches-from-old-photos) pass - Tears and missing corners — the [torn photo repair](/docs/how-to-repair-torn-photos) workflow - Fading and sepia cast — the [color restoration](/docs/how-to-fix-faded-photos) pass ### 3. Colorize (optional but powerful) A colorized ancestor portrait connects modern viewers to history in a way black-and-white rarely does. Pinin's [colorization engine](/colorization) adds natural, period-appropriate color — skin tones, clothing, backgrounds — automatically. It won't know your ancestor's true eye color, but the result looks like a photograph of a real person. ### 4. Enhance and upscale for sharing The [enhancement engine](/ai-enhancement) sharpens faces and upscales the image so you can print it, include it in a family tree book, or share it on genealogy sites. ### 5. Organize and back up Name files consistently (`surname-firstname-year`), record what you know about each person, and back up to two locations. Your restored ancestors deserve better than a drawer. ## FAQ **Can AI restore very old photos (1800s)?** Yes. The AI works on the image content, not the date. Tintypes, cartes de visite, and albumen prints digitized at high resolution restore well. Extremely damaged or faded originals may need manual restoration. **Is it okay to colorize historical photos?** Yes — and it's a popular, accepted practice in genealogy when labeled as colorized. The original black-and-white version remains the archival copy. **How much does it cost to restore family photos?** With Pinin, your first restoration is free. Each photo after that costs one credit — often less than a dollar per photo for standard restoration. **Where should I share restored ancestor photos?** Family sites (Ancestry, MyHeritage, FamilySearch), genealogy Facebook groups, and family group chats. A restored ancestor portrait is a gift the whole family can enjoy. Bring your ancestors back to life — [restore an ancestor photo now](/photo-restoration).


## How does AI photo colorization work? The science behind adding color to black & white photos

Source: https://pinin.ai/docs/how-does-ai-photo-colorization-work

# How Does AI Photo Colorization Work? The Science Behind Adding Color to Black & White Photos Colorizing a black & white photo used to be a painstaking manual craft: an artist would spend hours researching period clothing, matching skin tones, and painting color onto every square inch. Modern **AI photo colorization** does the same job in seconds — but how, exactly? The short answer: a neural network **guesses the most probable color for every pixel**, using patterns learned from millions of real color photos. The long answer — how the model sees your photo, what it learns, and why its guesses are sometimes wrong — is what this article covers. ## What the model actually sees A black & white photo is usually stored as a single channel of brightness. In image-processing terms, that's the **luminance** channel (Y). When the model processes your photo, it works in the **YCbCr color space**, where: - **Y** — luminance (brightness, what your grayscale photo contains) - **Cb** — blue-difference chrominance (how blue-ish vs yellow-ish a pixel is) - **Cr** — red-difference chrominance (how red-ish vs green-ish a pixel is) The model's job is to predict **Cb and Cr** for every pixel. Luminance is already known — the AI never invents lighting or shading. It only invents *color*, and it must make that color consistent with the existing brightness. A dark pixel can be dark red, dark blue, or dark green — the model has to figure out which. > **Key insight:** AI colorization is a *prediction* problem, not a *recovery* problem. The original colors are gone — the model is making an educated guess based on what the world usually looks like. ## The colorization pipeline Grayscale input Y channel only CNN feature extraction ResNet / EfficientNet Chrominance prediction Cb + Cr channels Color fusion and refinement Y + Cb + Cr → RGB The model predicts Cb/Cr from Y, then reconstructs full RGB color. ## Step 1 — Feature extraction (the CNN) The first stage is a **convolutional neural network (CNN)** — the same family of architectures used for object recognition. As the image passes through convolutional layers, the network builds up a hierarchy of features: 1. **Early layers** detect low-level patterns: edges, corners, gradients, noise. 2. **Middle layers** assemble those into textures: hair strands, fabric weave, skin pores. 3. **Deep layers** recognize *semantic objects*: faces, sky, grass, clothing, water. That semantic understanding is the key trick. The network doesn't color pixels in isolation — it identifies that a region *is sky* and therefore should be blue, or *is skin* and therefore should be within a narrow range of warm tones. ## Step 2 — Chrominance prediction With features extracted, the network outputs a **chrominance map**: predicted Cb and Cr values for every pixel, often at reduced resolution and then upsampled. This is where the magic — and the mistakes — happen: - The model is trained to minimize a loss function that penalizes wrong colors, so it learns to pick the *most probable* color given the context. - Many modern models use a **class-weighted loss**: instead of predicting a continuous color value, they predict a probability distribution over a quantized palette (e.g., 313 color classes). Rare colors are weighted higher so the output doesn't collapse to gray or sepia. - Because the network is rewarded for *plausibility*, it will choose a "safe" color when uncertain — which is why old AI colorizations often look muted, and why newer models still struggle with unusual objects. > **Why do some photos colorize better than others?** The model relies on context. A photo of a person on a beach gives strong cues (skin, sky, sand). A photo of an unfamiliar machine in a dark warehouse gives almost none — the model has to guess, and the result can look flat or wrong. ## Step 3 — Color fusion and output Finally, the predicted Cb and Cr channels are merged with the original Y channel, converted back to RGB, and — in most production systems including Pinin — passed through a **refinement pass** that cleans up color bleeding at edges and restores texture detail that the color layers might have smeared. ## Why the training data matters An AI colorizer is only as good as its training set. Pinin's colorization engine is trained on large, curated collections of **color photographs spanning decades** — which matters because color film itself changed over time: | Era | Typical color characteristics | | --- | --- | | 1900s–1930s | Hand-tinted, muted, limited palette | | 1940s–1960s | Kodachrome-style: saturated, warm reds | | 1970s–1990s | Cooler casts, more neutral skin tones | A model trained mostly on modern digital photos will "modernize" old photos. Training on era-appropriate imagery helps the model produce colors that look right for the period. ![Colorization before](https://assets.pinin.ai/0homepage_restored_photo_before.png?w=800&h=600&fit=crop&auto=format&q=80) ![Colorization after](https://assets.pinin.ai/0homepage_restored_photo_after.png?w=800&h=600&fit=crop&auto=format&q=80) ## The honest limits of AI colorization - **It doesn't know the actual colors.** Your grandmother's dress might have been burgundy; the model might choose navy because it's statistically more common. For family history, treat AI color as *an interpretation*, not a documentary record. - **Skin tones can drift** on heavily damaged photos, because the damage disrupts the features the model relies on. Running scratch removal first usually helps. - **Uncommon scenes get generic colors.** A rare car, an unusual uniform, or a custom-painted wall will often come out in generic, plausible colors. - **Text and fine detail can bleed.** Small lettering or intricate patterns may lose contrast after colorization. ## AI colorization vs manual colorization | | AI colorization | Manual colorization | | --- | --- | --- | | Speed | Seconds per photo | Hours to days per photo | | Cost | One credit per photo | $50–$500+ per photo (professional) | | Accuracy | Statistically plausible | Can match researched historical colors | | Subjectivity | Consistent, deterministic | Depends on the artist | | Learning curve | None | Years of practice | | Best for | Large collections, drafts, quick results | Hero prints, museum work, exact color | For most people and most photos, AI colorization is the right tool. If you need *exact* historical color (e.g., a museum exhibit), a professional colorist is irreplaceable — but for a family album, AI gets you 90% of the way there in 1% of the time. ## FAQ ### Is AI colorization accurate? It's *plausible*, not factual. The model predicts the most probable color for each object based on training data. Skin, sky, foliage, and other common subjects usually look convincing; rare or ambiguous subjects may get generic colors. ### Why do some black & white photos colorize better than others? Quality depends on the source: higher resolution, cleaner scans, and scenes with recognizable objects produce better results. Heavily damaged or extremely noisy photos give the model less to work with. ### Do I need to manually fix anything before colorizing? Ideally, yes. Run **Scratch Removal** first, then colorize. Clean edges help the color model avoid bleeding color across scratches and tears. See the [step-by-step colorization guide](/guides/how-to-colorize-black-and-white-photos) for the full workflow. ### Can AI colorize a photo that's already partly colored? Usually, yes — but results vary. If the existing color is faded, the model may treat it as grayscale anyway. If the photo is sepia-toned, the model typically preserves the warmth rather than guessing entirely new colors. ## Try it on your own black & white photos The fastest way to understand AI colorization is to see it on *your* photos. Upload a grayscale scan and let the model do its thing → **[Colorize a photo now](/photo-restoration)**.


## How to upscale and enhance old photos without losing quality

Source: https://pinin.ai/docs/how-to-upscale-and-enhance-old-photos

# How to Upscale and Enhance Old Photos Without Losing Quality Every old photo has a ceiling: the resolution it was captured or scanned at. Print a 1-megapixel scan at 8×10 inches and you get visible pixelation — blocky edges, soft faces, and that unmistakable "digital zoom" look. **AI upscaling** (super-resolution) raises that ceiling by *synthesizing* the detail that isn't there, so small photos can be printed large without falling apart. This guide covers how super-resolution works, what it can and cannot recover, and how to prepare your scans so the upscaler has the best possible starting point. ## First, the resolution basics Image resolution is measured in **pixels** (e.g., 3000 × 2000) or as a **pixel count** (6 megapixels). What matters for printing is *pixels per inch* (PPI) at the final print size: | Print size | Minimum pixels (at 300 PPI) | Equivalent scan | | --- | --- | --- | | 4×6 in | 1,200 × 1,800 (~2 MP) | 300 DPI scan of a 4×6 print | | 5×7 in | 1,500 × 2,100 (~3 MP) | 300 DPI scan of a 5×7 print | | 8×10 in | 2,400 × 3,000 (~7 MP) | 600 DPI scan of a 4×5 print | | 11×14 in | 3,300 × 4,200 (~14 MP) | 600 DPI scan of a 5×7 print | | 16×20 in | 4,800 × 6,000 (~29 MP) | 600 DPI scan of an 8×10 print | If your source doesn't have enough pixels for the print you want, you have two options: rescan at higher DPI (if the original print is available), or **upscale with AI**. > **Rule of thumb:** Always scan at the highest DPI you can — 600 DPI for prints up to 8×10. AI upscaling is a *rescue* tool, not an excuse to scan lazily. Garbage in, slightly better garbage out. ## What "naive" upscaling does (and why it fails) Traditional resizing — **bicubic interpolation** — simply invents intermediate pixels by averaging neighbors. It's fast and fine for small enlargements, but it has a hard limit: - **2× bicubic** looks acceptable on most photos - **3×+ bicubic** produces soft, smeared edges and halos - **4×+ bicubic** is a blurry mess No amount of sharpening fixes this, because the detail was never captured. Sharpening a blurry upscale just makes a *crisper-looking blur*. ## How AI super-resolution is different Super-resolution models are trained on millions of **low-resolution / high-resolution image pairs**. At inference time the model doesn't average pixels — it *recognizes patterns* (texture, edges, faces, hair, foliage) and synthesizes plausible high-frequency detail to match them. Low-res input e.g. 640×480 Feature extraction (CNN backbone) Edges, textures, shapes Detail synthesis (GAN / diffusion) Hallucinated HF detail Reconstruction and refinement 2× / 4× output Output 2560×1920 The model synthesizes plausible high-frequency detail from learned priors — not magic, but remarkably close on real photographs. Two model families dominate modern super-resolution: 1. **GAN-based models (e.g., ESRGAN-style)** — use a generator network trained against a discriminator. They produce crisp, texture-rich output but can occasionally add hallucinated detail (e.g., fabric patterns that don't match the original). 2. **Diffusion-based models** — iteratively denoise the image while upscaling. They tend to be more conservative and realistic, at the cost of more compute. Pinin's upscaling engine is tuned for **photographs** — meaning it knows what skin, hair, and clothing look like, and it prioritizes natural faces over aggressive sharpening. > **What super-resolution cannot do:** it cannot recover a face that was never in focus, read text that was never legible, or reveal details that the original never contained. If the information isn't in the pixels, the model has nothing to anchor its synthesis to — it will invent *plausible-looking* detail, which may or may not be accurate. ![Upscaling before](https://assets.pinin.ai/0homepage_upscaling_before.png?w=800&h=600&fit=crop&auto=format&q=80) ![Upscaling after](https://assets.pinin.ai/0homepage_upscaling_after.png?w=800&h=600&fit=crop&auto=format&q=80) ## When to use AI upscaling (and when not to) | Situation | AI upscaling helps? | Why | | --- | --- | --- | | 1 MP photo → 8×10 print | ✅ Yes | 2×–4× upscale produces print-quality detail | | Web image → large poster | ✅ Usually | Better than any alternative, with minor hallucination risk | | Newspaper clipping → readable text | ⚠️ Sometimes | Works best for headlines; body text stays soft | | Severely out-of-focus face | ❌ No | Blur removal first, then upscale — see [how to fix blurry photos](/guides/how-to-fix-blurry-photos) | | Tiny 100×100 crop of a face | ❌ No | Not enough information; results look synthetic | | Museum-grade archival print | ❌ No | Use a professional scan and conservator instead | ## Resolution myths, debunked - **"Upscaling adds new detail."** No — it *synthesizes* plausible detail. On photographs this looks like detail, but it's an interpretation. That's fine for prints and screens; it's not fine for forensic or archival use. - **"More sharpening after upscaling helps."** Usually the opposite. AI-upscaled images already have sharp edges; extra unsharp mask creates halos and noise. If it looks crunchy, dial the sharpening down. - **"Higher DPI scans don't matter if I have AI."** They absolutely do. A 600 DPI scan contains 4× the information of a 300 DPI scan, and every restoration engine — including upscaling — performs better with more input detail. - **"All upscalers are the same."** No. Generic upscalers sharpen everything, including noise and grain. Photo-tuned upscalers (like Pinin's) preserve skin texture and avoid amplifying film grain into crusty artifacts. ## Preparing a batch of photos for upscaling If you're processing an entire archive, a little preparation goes a long way: 1. **Scan consistently** — use the same DPI (600 is a good default) and the same color profile across the batch. 2. **Clean the scanner glass** — dust shows up as bright specks that the upscaler will happily sharpen into stars. 3. **Crop to the photo** — don't include the white border; it wastes resolution and can confuse color handling. 4. **Group by condition** — scan clean photos in one pass, damaged ones in another, so per-batch settings stay consistent. 5. **Run restoration first, upscaling last** — scratch removal and colorization change the image; upscale once, at the end, at the size you actually need. For the full archive workflow, see our [batch photo restoration guide](/docs/batch-photo-restoration-guide) and the [digitization guide](/guides/how-to-digitize-old-photos). > **Which upscale factor?** Pick the smallest factor that meets your goal. A 2× upscale of a good scan is usually indistinguishable from native resolution; 4× is great for prints; 8× is for extreme cases and carries the highest hallucination risk. You can always upscale more later — you can't un-invent detail you don't like. ## FAQ ### Will AI upscaling make my photo look sharper? Yes — that's its main job. It sharpens edges, restores texture, and reduces the blockiness of low-resolution sources. The catch: it's synthesizing detail, so the "sharpness" is a reconstruction, not the original optical detail. ### Can upscaling fix an out-of-focus photo? Not by itself. Upscaling assumes the blur is *resolution-related*. True defocus or motion blur needs a blur-removal model first. Fix the blur, then upscale — Pinin's [Blur Removal](/photo-restoration) engine handles this before you upscale. ### What's the largest size I can print an upscaled photo? There's no hard limit — a 2 MP scan upscaled 4× becomes ~32 MP, enough for a 24×36 inch print at 200 PPI. The practical limit is how much synthetic detail you're comfortable with. For wall prints, most people are happy up to 4×. ### Does upscaling work better on scanned photos or phone photos of prints? Scanned photos, by far. A scan captures the print's tonal range cleanly. A phone photo of a print adds glare, perspective distortion, and sensor noise — all of which the upscaler will amplify. If the original print exists, [scan it properly](/guides/how-to-scan-photos-for-restoration) first. ## Upscale a photo that deserves it If you have a small, soft photo that you've always wanted to print big, this is the tool. **[Upscale your photo now](/photo-restoration)** — one credit per photo, free credit for new users.


## AI photo restoration troubleshooting: why your results look wrong and how to fix them

Source: https://pinin.ai/docs/ai-photo-restoration-troubleshooting

# AI Photo Restoration Troubleshooting: Why Your Results Look Wrong and How to Fix Them AI photo restoration is remarkably good — and occasionally frustrating. A photo comes back with a waxy face, an orange cast, or blocky artifacts, and it's tempting to blame the tool. In most cases, though, the root cause is diagnosable: the source scan, the engine order, or a mismatch between the photo and the engine. This guide covers the most common failure modes, what causes them, and exactly how to fix them. ## First: diagnose before you re-run Most bad results fall into one of a handful of patterns. Find your symptom in the table, then jump to the fix. Problem visible? Faces: waxy, plastic, distorted Cause: Source too small or over-compressed Fix: Rescan at 600 DPI, run face enhancement only Colors: wrong hue, oversaturated, purple sky Cause: Model guessing on ambiguous grayscale regions Fix: Accept limitation or use manual colorization for critical work Artifacts: grid, blocky, ringing Cause: JPEG artifacts amplified by upscaler Fix: Save source as PNG or TIFF, lower upscale ## The symptom table | Symptom | Root cause | Fix | | --- | --- | --- | | Faces look blurry or smeared after restore | Source face is too low-res for the engine | Run **Upscaling first**, then Face Enhancement. If still soft, rescan at 600 DPI. | | Blocky "jpeg" artifacts everywhere | Heavy JPG compression in the source | Re-scan or re-export from the original as PNG/TIFF. Run Upscaling to smooth blocks. | | Orange / yellow cast on the result | Faded print scanned with warm cast; color engine amplified it | Run **White Balance** first, then re-run color engines. | | Colors look oversaturated / neon | Colorization engine applied to an already-colored (faded) photo | Only colorize true grayscale. For faded color, use White Balance + Upscaling instead. | | Group photo: some faces good, some bad | Faces are small relative to the frame | Crop each face region, restore separately, then merge — or use a higher-DPI scan. | | Dark areas lost all detail | Source shadows are crushed (underexposed scan) | Rescan with a longer exposure / higher dynamic range; avoid brightening in post. | | Skin looks waxy / plastic | Face Enhancement over-applied on a low-detail source | Reduce engine strength if available, or upscale the source first. | | Photo came back looking "painted" | Source was already heavily processed (old reprint) | Use the original print if you have it; avoid stacking too many engines. | ## Symptom deep-dive #1: Blurry faces after restoration ![Blurry photo before restoration](https://assets.pinin.ai/0homepage_blur_removal_before.png?w=800&h=600&fit=crop&auto=format&q=80) This is the most common complaint, and it's almost always a **source resolution** problem, not a model failure. A face that occupies only 5% of a 1 MP photo is about 120×90 pixels — roughly 40×30 pixels of actual facial features after accounting for hair and background. No model can conjure a crisp face from that. **Run these checks in order:** 1. **Measure the face in the source.** If the face region is under ~150 pixels wide, the photo is below the practical threshold for good face restoration. 2. **Check the scan DPI.** A 300 DPI scan of a 4×6 print gives you a 1,200×1,800 image — fine for the whole photo, marginal for faces. Rescan at 600 DPI if the original print is available. 3. **Reorder the pipeline.** Run **Upscaling (2×–4×) first**, then **Face Enhancement**. The face model gets 4–16× more pixels to work with. 4. **As a last resort, crop.** Restore the face as its own crop, then paste it back into the full photo in any basic editor. > **Warning:** Do *not* use Face Enhancement repeatedly on the same photo hoping it will converge on a sharper face. Each pass adds smoothing, and you'll end up with a waxy mask. One pass, on the best source you can produce. ## Symptom deep-dive #2: Wrong colors and color casts Color problems come in two flavors: the **yellowed/faded cast** (a scanning or aging problem) and the **wrong hue** (a colorization or engine-order problem). They have different fixes. **For a yellowed, faded print:** 1. Run **White Balance** first and check the result *before* running anything else. 2. If the cast persists, the scan itself may be biased — rescan with the scanner's color profile set to a neutral daylight balance. 3. Only then run Colorization (if it's truly grayscale) or Upscaling (if it's a faded color photo). **For wrong hues after colorization:** 1. Confirm the source is **actually grayscale**. Colorizing an already-colored photo produces doubled, muddy color. 2. Run **Scratch Removal** before colorizing — scratches and stains get "colored" as if they were real objects. 3. Check for strong lighting gradients in the source; a heavily shadowed face will often be assigned flat, generic skin tones. A flatter, well-lit scan will colorize more accurately. > **Tip:** Keep a copy of the untouched original. If a run goes sideways, you can always restart from the original rather than from a degraded intermediate. Pinin never overwrites your upload — but you should still keep your own master file. ## Avoiding the problems in the first place Most troubleshooting disappears if you follow a consistent pipeline: 1. **Scan well** — 600 DPI, clean glass, neutral color profile, no auto-enhance filters. See the [scanning guide](/guides/how-to-scan-photos-for-restoration). 2. **Run one engine at a time**, and inspect after each step instead of stacking everything blindly. 3. **Fix the photo, then the color, then the size** — damage repair before color, color before upscaling. 4. **Match the engine to the damage.** Colorization doesn't fix scratches; Upscaling doesn't fix fading. When in doubt, the [getting started guide](/docs/getting-started-with-ai-photo-restoration) has a damage-type table. ## FAQ ### Why does my restored photo look blurrier than the original? Either the source was already at its resolution limit (a low-res scan), or an enhancement engine smoothed away fine detail. Fix: re-scan at higher DPI, upscale before enhancing, and avoid running multiple smoothing engines in sequence. ### Can I fix a restoration that already looks bad? Yes — you're never stuck with a result. Restart from the untouched original, change the engine order (usually: scratch removal → white balance → color → upscale → face), and re-run. Each restoration is a separate credit, but you keep the original forever. ### Why did the sky turn purple / the grass turn blue? Colorization guesses from context. If the source has a color cast, a heavy grain, or was partially faded, the model's cues get scrambled. Fix the cast with White Balance first, then re-colorize. On heavily damaged photos, run Scratch Removal first too. ### Do I need a paid plan to retry a failed restoration? No — retries use credits like any restoration, and the free credit works the same way. If a restoration consistently fails on a given photo, it's almost always a source-quality issue rather than a credit issue. Improve the scan, then retry. ## Fix your photo with a clean pipeline Don't fight a bad result — restart from the original with the right engine order. **[Start a fresh restoration](/photo-restoration)**.


## Batch photo restoration: how to restore multiple old photos at once

Source: https://pinin.ai/docs/batch-photo-restoration-guide

# Batch Photo Restoration: How to Restore Multiple Old Photos at Once Restoring one photo is satisfying. Restoring three hundred is a project. The family album, the wedding archive, the genealogy stack, the dealer's inventory — these are the cases where **batch photo restoration** earns its keep, because the alternative is either hundreds of hours of manual editing or letting most of the collection stay damaged. This guide covers how batch restoration works, how to prepare a batch so the results are consistent, and where AI still needs a human in the loop. ## How batch restoration works Batch restoration in Pinin follows a simple pipeline: upload many photos, let the system analyze them, restore them in parallel, review, and download. Upload folder 10–100s of photos Auto damage detection per-photo analysis Engine application parallel processing QA review flag failures Each photo gets independent engine selection — you set the policy, AI executes. The key difference from single-photo restoration: you **set the rules once** (which engines to apply, what strength) and the system applies them consistently across the whole batch. That consistency is exactly what archives need — and exactly what makes a batch of mixed-quality photos tricky. ## Batch vs single restoration | | Single photo | Batch | | --- | --- | --- | | Time per photo | ~1 minute of your attention | Seconds of your attention (AI does the rest) | | Cost | 1 credit per engine per photo | Same per-photo cost, no markup | | Consistency | Varies — you may tweak settings per photo | Uniform — same settings across all photos | | User effort | High (per-photo decisions) | Low (decide once) | | Edit granularity | Full control per photo | Coarse — you review and re-run stragglers | | Best for | Hero shots, tricky damage | Albums, archives, inventory | > **The honest trade-off:** batch restoration trades per-photo control for speed and consistency. The workflow that works is: batch everything, then hand-fix the 5–10% of photos that need individual attention. ## Who batch restoration is for - **Family archivists** — grandma's shoebox: 400 photos, 60 years, every damage type in existence. Batch gets the collection to "viewable and shareable" in an afternoon, then you can hero-restore the important ones. - **Wedding and event photographers** — delivering a restored, consistent set of vintage-style shots from a client's old negatives or prints. - **Genealogists and family historians** — sourcing photos from dozens of relatives; batch processing normalizes them so faces are recognizable and details (uniforms, house numbers, storefronts) are legible. - **Antique dealers and auction houses** — inventory photos that need to look their best in listings, without a retoucher on staff. ![Scratch removal before](https://assets.pinin.ai/0homepage_scratch_removal_before.png?w=800&h=600&fit=crop&auto=format&q=80) ![Scratch removal after](https://assets.pinin.ai/0homepage_scratch_removal_after.png?w=800&h=600&fit=crop&auto=format&q=80) ## Preparing a batch for consistent results The single biggest factor in batch quality is **how uniform your inputs are**. A batch scanned the same way, at the same DPI, with the same color profile will come back looking like a coherent set. A batch thrown together from phone photos, old scans, and photocopies will come back... like that, but restored. **Pre-flight checklist:** 1. **Consistent DPI** — scan the whole album in one sitting at the same DPI. 600 DPI for prints up to 8×10, 300 DPI for larger prints. 2. **Consistent color profile** — use the scanner's neutral/default profile for the whole batch. Don't let the scanner auto-correct some photos and not others. 3. **One damage type per batch, when possible** — if 80% of the album has scratches and 20% has fading, run two batches with different engine sets. Uniform batches get uniform results. 4. **Crop borders and remove duplicates** — borders confuse color handling, and duplicates waste credits. 5. **Straighten and dedupe before uploading** — a tilted scan wastes upscaling power on empty corners. 6. **Keep a naming convention** — `album_01_001.tif` beats `IMG_2048.JPG` when you're reviewing 300 results. > **Tip for mixed collections:** If your photos vary wildly in condition, let the auto-detection pass sort them. Review the damage report, then split into sub-batches by engine set — scratches in one, fading in another, colorization candidates in a third. This gets you batch speed *and* near-single-photo quality. ## The review workflow Batch doesn't mean unattended. The professional workflow has a QA step: 1. **Let the batch finish**, then skim the contact sheet (thumbnails of all results). 2. **Flag anything that looks wrong** — faces, color casts, artifacts. 3. **Re-run flagged photos individually** with adjusted settings (or improved sources). 4. **Archive the results** with a clear folder structure: originals untouched, restored output, and rejected/needs-work separated. Expect roughly **5–10% of photos** to need individual attention on a typical mixed-condition album. That's not a failure — that's the workflow working. ## FAQ ### Can I restore 50 photos at once? Yes. Pinin's batch processing handles dozens of photos per run, analyzing each one independently and applying the selected engines in parallel. Fifty photos is a normal batch — the workflow is the same as for five. ### Does batch cost less per photo? No — batch pricing is the same per photo as single restoration (one credit per engine per photo). What batch saves you is *time and attention*, not credits. For most projects that's the expensive resource anyway. ### What if different photos need different engines? Let the **auto-damage detection** pass suggest per-photo engines, or split the batch by condition. Photos with scratches get the scratch engine, faded photos get color correction, grayscale photos get colorization. Uniform sub-batches produce the most consistent results. ### Should I batch-restore or restore one by one? Batch for volume, single for heroes. Run the whole collection through a batch to get everything to a baseline quality, then hand-restore the handful of photos that matter most — the wedding portrait, the only photo of a grandparent. The batch keeps the project moving; the singles make the highlights perfect. ## Start your archive project Whether it's a shoebox or a warehouse, batch restoration turns an overwhelming project into an afternoon. **[Restore your first batch](/photo-restoration)** — free credit included.


## How Pinin credits work: understand photo restoration pricing and cost

Source: https://pinin.ai/docs/how-pinin-credits-work

# How Pinin Credits Work: Understand Photo Restoration Pricing and Cost Pinin doesn't charge per photo or per download — it uses a **credit system**. One credit restores one photo with one engine. Simple, predictable, and the same price whether you're restoring one photo or a thousand. This guide explains exactly how credits work: what they cost, what they're spent on, how the free credit differs from paid credits, and how to make every credit count. ## The credit model at a glance Every restoration you run deducts **one credit per engine per photo**. Colorize a photo? One credit. Colorize *and* remove scratches? Two credits. Run four engines on a single photo? Four credits. Buy a pack $10 = 100 credits Restore a photo 1 credit per image Credit deducted only on success Download HD/4K keep the original Unused credits never expire — keep them for your next project. ## What one credit buys ![Restored color photo](https://assets.pinin.ai/0homepage_restored_photo_after.png?w=800&h=600&fit=crop&auto=format&q=80) One credit processes **one image through one engine**. That's the entire pricing model — no per-megapixel charges, no hidden fees for larger files, no extra cost for 4K downloads. A single credit takes a faded black-and-white portrait through Colorization and gives you back a full-color version you can download in HD or 4K. | Engine | Credits per photo | | --- | --- | | Face Enhancement | 1 | | Colorization | 1 | | Upscaling | 1 | | Scratch Removal | 1 | | Blur Removal | 1 | | White Balance | 1 | There is no per-engine price difference and no charge for re-downloading a restored photo. Once restored, your result is yours to download in HD or 4K as many times as you like. > **Predictable budgeting:** a typical damaged family photo needs 2–3 engines — say Scratch Removal + Colorization + Upscaling = **3 credits** for a fully restored, print-ready file. Plan for 2–3 credits per photo and you'll almost never be surprised. ## Free credits vs paid credits | | Free credit | Paid credits | | --- | --- | --- | | Who gets it | New users, once | Anyone, any time | | Cost | $0 — no card required | Plans or refill packs | | What it's used for | Any single engine on one photo | Any engine, any photo | | Quality | Identical to paid | Identical to free | | Expiry | See plan terms | See plan terms | The free credit exists so you can verify the quality on *your own photo* before spending anything. The result is a full-resolution restoration — no watermark, no downgrade. ## Plans: Free, Giotto, Botticelli, and Da Vinci Pinin offers a free tier plus three paid plans. All paid plans include a pool of credits that refills **monthly**: | Plan | Credits per month | Best for | | --- | --- | --- | | Free | 1 welcome credit | Trying the service | | Giotto | ~30 | A family album or occasional restorations | | Botticelli | ~100 | Regular batches, larger collections | | Da Vinci | ~400 | Archives, photographers, dealers | Credits from your monthly plan allowance and credits from one-time refill packs sit in the same balance and are spent identically, but they have different expiry rules. Check the [pricing page](/pricing) for current plan prices and credit amounts. ## What happens to unused credits? - **Monthly plan credits** reset each billing cycle — unused subscription credits do **not** roll over to the next month. - **Refill-pack credits** stay valid for **12 months** from the purchase date, so they're the right choice for one-off projects. - Your balance is shown in the account dashboard, so you always know what you're working with before you start a batch. > **Tip:** For one-off projects, buy exactly what you need as a refill pack. For ongoing work (a growing archive, a photography side business), a monthly plan gives you a predictable budget and the best per-credit rate. ## Making every credit count 1. **Scan once, scan well.** A 600 DPI scan restores dramatically better than a 300 DPI scan — and better results mean fewer retries, which means fewer credits spent. 2. **Run engines in the right order.** Fix damage first, color second, upscale last. Running engines in the wrong order produces worse results and tempts you into wasteful retries. See the [troubleshooting guide](/docs/ai-photo-restoration-troubleshooting). 3. **Batch smart.** Restore similar photos together with the same engine set instead of re-deciding per photo. The [batch guide](/docs/batch-photo-restoration-guide) covers this in detail. 4. **Don't stack redundant engines.** Don't run Face Enhancement twice, and don't colorize photos that already have color. Each redundant pass is a credit spent making things worse. 5. **Test on one photo first.** Before committing a whole batch, restore the worst photo in the batch and check the quality. If it survives, the rest will be fine. ## FAQ ### Do credits expire? Yes, on different schedules. **Subscription credits** reset each billing cycle — whatever you don't use by the end of the month doesn't carry over. **Refill-pack credits** are valid for 12 months from purchase. Your balance and any expiry dates are always visible in the account dashboard, so you never lose track. ### Can I get a refund on unused credits? Refunds for unused refill credits are handled on a case-by-case basis — contact support from your account dashboard. Monthly plan allowances are tied to the billing cycle, so a mid-cycle cancellation applies to the *next* cycle, not the current one. ### Do I use a credit if the restoration fails? You only spend credits on restorations that actually run. If an upload fails, the image is rejected as unsupported, or the restoration errors out before completing, **no credit is deducted**. Failed or unusable outputs can be retried; if a photo consistently produces bad results, it's a source-quality problem — see the [troubleshooting guide](/docs/ai-photo-restoration-troubleshooting). ### How do I check my credit balance? Your current balance is shown in the account dashboard, next to the restore button. The balance updates immediately after every restoration and every purchase, so what you see is what you have. ## Restore your first photo — it's on us Create a free account and your first restoration is free: one credit, no card required. **[Start restoring](/photo-restoration)**.


## Getting started with AI photo restoration — a complete guide

Source: https://pinin.ai/docs/getting-started-with-ai-photo-restoration

# Getting Started with AI Photo Restoration — A Complete Guide If you have a box of old family photos sitting in a drawer — faded, scratched, or torn — you already know the feeling: these are the only copies of moments that can't be re-shot. The good news is that **AI photo restoration** has reached the point where a damaged photo can be brought back to life in minutes, without Photoshop skills and without paying a professional retoucher. This guide walks you through the entire process: what AI restoration can and can't do, which damage types are fixable, how to run your first restoration, and how to get the best possible result. ## What AI photo restoration actually does AI photo restoration uses **deep learning models** trained on millions of image pairs — damaged and clean versions of the same photo. When you upload a photo, the model analyzes the damage patterns and reconstructs the missing or degraded information: - It **detects** scratches, tears, stains, noise, and blur - It **infers** what the original pixels probably looked like - It **rebuilds** the affected regions while preserving faces, textures, and details The result is a restored copy that looks natural — not a filter slapped on top. > **Good to know:** AI restoration works best on photos that are *digitized properly*. If your source scan is low-resolution or badly lit, the restoration has less information to work with. See our [guide to scanning photos for restoration](/guides/how-to-scan-photos-for-restoration) before you start. ## What Pinin can fix Pinin has six specialized restoration engines, each trained for a specific type of damage: | Engine | Fixes | Best for | | --- | --- | --- | | Face Enhancement | Blurry or low-detail faces | Portraits where faces look soft or washed out | | Colorization | Grayscale photos | Black & white photos from before the 1960s | | Upscaling | Low resolution | Small prints, newspaper clippings, web images | | Scratch Removal | Scratches, dust, mold lines | Photos stored in albums or sleeves | | Blur Removal | Motion blur, out-of-focus blur | Slightly blurred snapshots | | White Balance | Yellowing, color casts, fading | Photos from the 1970s–90s with warm fading | Most damaged photos need **more than one engine** — a faded 1950s portrait might need colorization *and* scratch removal. Pinin lets you run multiple engines on the same photo, and each restoration costs one credit (see [how Pinin credits work](/docs/how-pinin-credits-work)). ## How the restoration pipeline works 1. Upload JPG, PNG, WEBP 2. Damage detect AI scans the photo 3. Engine select Face, color, scratch… 4. Restore ~seconds 5. Save HD or 4K Each engine runs independently, so you can combine them in any order. ## Your first restoration, step by step 1. **Scan or photograph the photo** at 300 DPI or higher — 600 DPI for small prints. If you're working from a phone photo of a print, use even lighting and shoot straight on. 2. **Open Pinin and upload** the image. JPEG, PNG, TIFF, BMP, and RAW are supported. 3. **Choose the engines** you need. Not sure? Start with the damage you can see: scratches → Scratch Removal, gray photo → Colorization, soft faces → Face Enhancement. 4. **Run the restoration.** Most restorations finish in seconds to a minute, depending on image size. 5. **Compare and download.** Pinin keeps the original untouched, so you can always compare before/after and download the restored version in HD or 4K. > **Tip:** Run the most aggressive fix last. If you plan to colorize *and* remove scratches, remove scratches first, then colorize — the color model sees cleaner edges and produces more accurate color. ## What AI photo restoration is NOT It's worth being clear about the limits, because a lot of marketing overpromises: - **It does not invent new content.** If a face is completely missing, AI cannot reconstruct it from imagination. - **It is not a face-swap or beauty filter.** The goal is to *recover* the original photo, not change who is in it. - **It is not magic for microscopic photos.** A 200×150 pixel scan has very little information to work with. - **It does not replace a professional conservator** for museum-grade archival work. When a photo is restorable, Pinin does an impressive job. When it isn't, no tool can help — and we'd rather be honest about that. ![Face restoration before](https://assets.pinin.ai/0homepage_face_restoration_before.png?w=800&h=600&fit=crop&auto=format&q=80) ![Face restoration after](https://assets.pinin.ai/0homepage_face_restoration_after.png?w=800&h=600&fit=crop&auto=format&q=80) ## Scan quality matters: DPI by photo size | Photo size | Minimum DPI | Resulting pixels (approx.) | | --- | --- | --- | | 4×6 in (10×15 cm) | 600 DPI | 2,400 × 3,600 | | 5×7 in (13×18 cm) | 600 DPI | 3,000 × 4,200 | | 8×10 in (20×25 cm) | 300 DPI | 2,400 × 3,000 | | 11×14 in (28×36 cm) | 300 DPI | 3,300 × 4,200 | Save scans as **TIFF or high-quality PNG** if you can; JPG at quality 90+ is acceptable. Avoid re-compressing the same JPG repeatedly — each generation loses detail. ## Privacy and security Your photos are personal. Pinin processes uploads securely and **does not use your photos to train models**, and originals are not shared with third parties. You can read the full details in the [docs](/docs), but the short version is: your family photos stay yours. > **Wondering about cost?** New users get a **free credit** — no credit card required — to restore their first photo. After that, restorations cost one credit per engine per photo. See [how Pinin credits work](/docs/how-pinin-credits-work) or visit our [pricing page](/pricing). ## FAQ ### Is AI restoration the same as AI upscaling? No. **Upscaling** increases resolution — it makes a small image bigger. **Restoration** repairs damage — scratches, tears, fading, blur. Pinin offers both, and they often work well together (upscale after restoring), but they solve different problems. ### Can it fix photos with both scratches and blur? Yes. Run **Scratch Removal** first, then **Blur Removal** (or Face Enhancement for portraits). Each engine is independent, so you can stack them on the same photo, spending one credit per engine. ### Does Pinin store or train on my photos? No. Pinin does not train on user uploads, and your originals are not shared with third parties. Uploads are processed for the restoration and remain private to your account. ### What's the difference between the free credit and paid credits? The **free credit** is a one-time welcome credit for new users — it lets you restore your first photo without paying and without entering a card. **Paid credits** come from plans or refill packs and are used the same way (one credit per engine per photo). There's no functional difference in quality; the free credit is simply on us. ## Ready to restore your first photo? If you've got a damaged photo sitting in a drawer, this is the moment. Restore it now → **[Start your first restoration](/photo-restoration)** — free credit included, no card required.