How to Restore Old Photos with AI (Without Ruining Them)

You found a box of old family photos in the attic. Some are fine. Some are cracked down the middle, faded to near-white, or spotted with water damage from a leak two decades ago. You know you should do something with them. You also know a professional restoration service quoted you $150 per photo.

AI can handle most of what's in that box for a fraction of that price.

Modern AI photo restoration tools can remove scratches, reverse color fading, sharpen blurry faces, and colorize black-and-white prints, all in under a minute per photo. But "can" and "should" aren't the same thing. There are specific ways to use these tools that produce results worth printing and framing, and specific mistakes that will make grandma's face look like a wax figure.

Here's what actually works.

What AI Photo Restoration Actually Does

AI restoration isn't a filter. It's a reconstruction process.

The model analyzes your damaged photo and predicts what the missing or degraded information was supposed to look like, drawing on patterns from millions of similar photos. A scratch across someone's shoulder? The AI fills it with clothing texture that matches the surrounding area. Faded sky? Restored to a plausible blue. Blurry face? The model sharpens edges based on typical face geometry.

The result is not a perfect recovery of the original. It's a high-quality reconstruction. For family photos, where the goal is "looks right and feels like them," that's usually good enough.

The Three Things That Go Wrong

Most bad AI restoration results come from the same three problems.

Over-sharpening. Aggressive sharpening on an old photo makes faces look plasticky and textures look digital. Good tools restore sharpness selectively. If a restored face looks airbrushed rather than sharp, the algorithm over-compensated.

Hallucinated faces. This is the big one. AI face enhancement works by predicting what a face should look like. On small, low-resolution faces (common in group photos from the 1950s and 60s), the model sometimes fills in features that don't match the actual person. The face looks plausible, just not like them. Always compare the restored version against the original before accepting it.

Color drift in colorization. Auto-colorization guesses colors from context. Grass is probably green, skin is probably warm brown, sky is probably blue. It goes wrong with unusual objects, certain hair dyes, or clothing in colors the model didn't expect. A red carpet colorized as brown, a yellow dress that comes out grey. There's no fix except accepting that colorization is approximate.

Before You Upload: Scan Properly

The single biggest factor in restoration quality is what you give the AI to work with.

If you photograph a printed photo with your phone, you're adding lens distortion, uneven lighting, and glare on top of the existing damage. That compounds what the AI has to fix.

Scan your photos instead. A flatbed scanner set to 600 DPI is the minimum for restoration work; 1200 DPI is better for small prints like wallet-size or 3×5. Libraries, FedEx Office locations, and most public archives have scanners available for a small fee or free. The Library of Congress recommends 400–600 DPI for preservation scanning, with higher resolution for smaller originals.

Save as TIFF or high-quality PNG before uploading. JPEG compression adds its own artifacts on top of the photo's existing damage.

How to Restore Old Photos with GetPhotoShoot

GetPhotoShoot's restore old photos tool is built for exactly this use case: family archive photos, graduation portraits, wedding photos that have yellowed, childhood snapshots with water stains.

Here's the process:

1. Upload your scan. Drop your scanned image into the upload area. The tool accepts JPEG, PNG, and TIFF files.

2. Let the AI analyze the damage. The model identifies scratches, fading, and noise automatically. No parameters to configure, nothing to mark by hand.

3. Review the preview. This step matters. Compare the restored version against your original, specifically looking at faces. Check that proportions match. Check that skin tones look right for the person in the photo.

4. Download or adjust. If the result looks good, download at full resolution. If a face looks off, try uploading a higher-resolution scan before assuming the tool can't handle it.

The advantage of a dedicated restoration tool over a general AI image editor is that the model is specifically trained on photo damage patterns, not general image generation. You get fewer hallucinated details and better fading correction.

Colorization: Set Realistic Expectations

Colorizing a black-and-white photo is different from restoring a damaged color photo. Restoration recovers information that was there. Colorization invents information that was never recorded.

That said, modern colorization is often striking. Skin tones, outdoor scenes, and everyday fabrics tend to colorize convincingly. The model also pulls from historical context, so a 1940s photo gets period-appropriate color treatment.

Where it stumbles: small details, unusual colors, and interior scenes with artificial lighting. If your grandfather's sweater was a particular shade of burgundy, the AI will guess. It might be close. It might not be.

For family photos, the result is usually close enough to feel real. For archival or historical research, treat colorization as an approximation, not a record.

When AI Restoration Isn't the Right Tool

Photos that are more than 70–80% destroyed (severely water-soaked images with lifted emulsion, prints faded to near-blank) are beyond what current AI handles well. The model needs enough original information to reconstruct from. When that's mostly gone, it generates rather than restores, and the result won't look like the original.

For high-stakes archival work (estate photos for legal purposes, historical documentation, images of significant cultural value), a human professional conservator is the right call. The American Institute for Conservation maintains a directory of certified photograph conservators.

For the shoebox of family photos most of us have, AI does the job well and does it fast.

A Practical Workflow for a Box of Old Photos

If you're working through a collection rather than a single photo, batching your workflow saves time:

1. Sort by damage level: light fading, scratches only, color damage, heavily damaged
2. Scan the whole batch first (set up your scanner once, run them all through)
3. Run AI restoration on light and moderate damage photos in bulk
4. Manually review each restored face before saving
5. Set aside heavily damaged photos for a separate decision: AI, professional conservator, or accept as-is

GetPhotoShoot's restore old photos page handles the middle of that workflow well. You can move through a batch without re-configuring anything between uploads.

Once you have restored photos, save them in at least two places: a local hard drive and a cloud backup. The reason you're restoring them is because originals get damaged. Don't create a situation where the only copy is also vulnerable.

The Part People Skip

Restored photos sitting in a folder you never open aren't actually preserved.

Print the ones that matter. A 4×6 print costs under a dollar. A photo book costs $20–40. The goal isn't a perfect digital archive. The goal is a photo of your grandmother on the wall where people can see it.

If you want to do more with your photos beyond restoration, the same approach applies to getting new ones: tools like GetPhotoShoot handle professional headshots and creative portraits from current photos, and the best AI headshot generators comparison covers your options if you're updating your professional image alongside cleaning up the archives.