Best AI Photo Restoration Tools: Old Photos, Scratches and Face Fidelity Compared

Published on September 16, 2026 by Steven Jones

The best AI photo restoration tool is not the one that creates the sharpest face. It is the one that removes visible damage while changing as little as possible about the person, clothing, setting, and text that were actually in the photograph. For an old family portrait, a slightly soft but faithful result is usually more valuable than a polished reconstruction of someone who never existed.

This comparison focuses on repairing an existing damaged photograph: scratches, creases, fading, blur and age-related deterioration. It deliberately separates restoration from upscaling and from generating a new family composition, because those jobs reward different behaviour from the AI.

If you already have a scan to repair, try restoring your own photo with DIY AI Studio. Start with gentle restoration, keep the original scan, and compare faces and fine details before accepting the result.

Quick verdict: choose the tool by how much invention you can tolerate

Tool	Best fit	Restoration approach	Main face-fidelity concern
DIY AI Studio	Controlled one-off restoration	Upload-led workflow with restoration strength and colour treatment	Stronger settings can invent plausible facial or texture detail
MyHeritage	Family-history photos and small faces	Face detection, enhancement, colourisation and Reimagine restoration tools	Face enhancement is a simulation when source detail is missing
Remini	Blurry portraits where the face is the main problem	Aggressive face and clarity enhancement	Can make a face look impressively detailed without proving those details are original
VanceAI	Scratches, tears, stains and scanned albums	Automated damage repair plus face enhancement and batch options	Automatic face sharpening still needs comparison with the source
Fotor	Bulk browser-based restoration	Automatic repair, colourisation and batch processing	Large missing areas require generated replacement detail
Adobe Photoshop	Historically important photos and manual control	Layered retouching, masks, healing tools and optional AI filters	Lowest invention risk when AI is used selectively, but requires more skill

This is intentionally not a numerical leaderboard. DIY AI publishes its wider testing methodology and datasets, including the AI image generation dataset, but those scores measure general image generation and editing. They do not measure historical fidelity. A restoration score needs different priorities, with identity retention and evidence preservation weighted above visual drama.

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Restoration and reconstruction are different jobs

AI restoration becomes risky at exactly the point where it looks most magical. A scratch across a plain wall can often be filled from surrounding pixels. A missing eye, mouth corner or line of handwritten text cannot. If the source lacks enough information, the model has to infer a plausible replacement.

MyHeritage is unusually clear about this in its Photo Enhancer documentation: enhanced faces are algorithmic simulations and may be inaccurate or distorted. That warning should be applied to every generative restoration tool, not just MyHeritage.

A recurring complaint from people restoring family photographs is that a result can look cleaner and still be worse. Eyes become a different shape. Teeth appear where the original mouth was closed. Hairlines move. Clothing is redesigned. A baby acquires adult-like facial detail. These are not cosmetic flaws in a restoration. They show the model has moved from repair to reconstruction.

How a photo-restoration comparison should actually be tested

The testing framework for this category needs more than one attractive portrait. It should use four permissioned images because each exposes a different failure mode: a scratched portrait, a faded colour photograph, a group photo with small faces, and a damaged image containing lettering. Until we publish those source images and outputs, we do not assign numerical restoration scores.

Test image	What the AI should repair	What must survive unchanged	Automatic rejection
Scratched portrait	Scratches, dust, crease marks, mild fading	Eye shape, nose, mouth, hairline, expression	A cleaner image with a recognisably different face
Faded colour photo	Colour cast, weak contrast, surface deterioration	Original clothing colours where evidence remains, skin tone relationships, background objects	Modern-looking colour treatment that invents scene details
Group photo	Global damage and softness	Every face, not only the largest subject	Small faces replaced by generic or repeated facial patterns
Photo with lettering	Damage around signs, captions or labels	Every readable character	Letters silently rewritten into different words, dates or names

The rejected outputs matter as much as the best one. A restoration comparison that shows only attractive after-images rewards the model for being persuasive. The more useful question is whether the output still matches the source when you inspect facial landmarks, hands, jewellery, seams, architecture, and text at high zoom.

DIY AI Studio: best first test when you want a gentler restoration

DIY AI Studio is designed around an existing photo rather than a blank generative canvas. You upload a scan, choose restoration strength and colour treatment, and can tell the model to preserve the same subjects, setting, composition and colours while repairing visible damage.

The useful part is the restraint. Start on the gentle setting for family faces and historically important details, then increase processing only if the result is still too damaged. Studio also tells users directly that AI may change faces and fine details, which is preferable to presenting generated detail as recovered fact.

There is a practical limitation: the working reference is resized to a maximum of 1024 pixels on the longest edge. That is appropriate for restoration decisions, but it should not be confused with print enlargement. If your main problem is making a clean image large enough for a poster or high-resolution print, use the AI image upscaler comparison after the restoration is approved.

MyHeritage: best fit for genealogy and crowded family photographs

MyHeritage makes sense when the photograph is part of a family-history workflow rather than a standalone editing job. Its enhancer detects faces and lets you inspect them individually, which helps in group photographs where the biggest risk is focusing on the central subject while smaller faces are quietly altered.

Its Reimagine tools also cover scanning, scratch and crease repair, colourisation and enhancement. The trade-off is that face enhancement is explicitly inferential. Use the face-by-face view as an audit tool, not proof that the sharper version is historically accurate.

Remini: useful for blur, but treat reconstructed faces cautiously

Remini is a face-first choice. Its restoration workflow emphasises sharpening faces and turning blurry, low-quality portraits into clearer images. That helps when a scan is soft, but the face structure is still visible.

The same strength creates its main limitation. The less facial information the original contains, the more freedom an enhancer has to create believable features. If the restored portrait suddenly has crisp eyelashes, teeth or skin texture that cannot be traced back to the scan, treat those details as generated. For sentimental family photographs, recognisability is a stricter test than sharpness.

VanceAI and Fotor: better suited to visible damage and album-sized jobs

VanceAI focuses on the physical defects people usually mean by restoration: scratches, tears, stains, creases, and faded colour. It also offers a Windows batch workflow, making it more practical than a one-photo mobile app when you have already scanned an album.

Fotor follows a similar automated route and adds batch restoration for larger collections. It is a sensible browser option if you want damage repair, colourisation and general photo editing in one place. With both tools, inspect face enhancement separately from scratch removal. A tool can clean paper damage and still over-process a face.

Photoshop: best when accuracy matters more than one-click speed

Photoshop remains the safer route for photographs where provenance matters because you can separate operations. Remove a crease with healing or cloning, correct faded colour on adjustment layers, mask changes to a small region and use AI only where it has a clear job. You do not have to accept a full-frame reinterpretation just to fix one damaged corner.

The cost is time and skill. For one lightly scratched family print, a dedicated restorer is faster. For a rare portrait, military photograph, handwritten image, or archival record, manual control is often worth more than automation because you can inspect and reverse every edit.

The safest restoration workflow is deliberately boring

1. Digitise first. Use the best scan available, keep the untouched master and avoid starting from a compressed social-media copy.
2. Repair physical damage before enhancing faces. Scratches, dust, contrast and fading are easier to judge than invented facial detail.
3. Use the weakest acceptable face enhancement. Compare eyes, nose, mouth, ears, hairline and expression against the original at high zoom.
4. Colourise last. Colourisation is an interpretation unless you have external evidence for the original colours. Keep a restored black-and-white version as the documentary copy.

If a photo contains lettering, dates, badges or signs, mask those areas or repair around them manually. Generative models can turn damaged text into plausible-looking but incorrect characters, which is unacceptable when the words themselves are part of the historical record.

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Do not pay for a long subscription just to fix one photo

Photo restoration is often a one-off job. That changes the buying decision. A weekly or monthly plan may make sense while digitising an album, but it can be poor value if you only need one framed photograph repaired for a family gift.

For a single photo, start with a tool that lets you inspect a result with minimal commitment. For dozens of scans, batch processing matters more. For historically important images, spend the budget on control and manual review rather than chasing the fastest automatic result.

And keep the task boundaries clean. If you want to merge relatives from separate photographs, use an AI image combiner. If you want to create a new portrait that never existed, use an AI family portrait generator. Don’t judge either by the same standard as restoring documentary evidence.

Which AI photo restoration tool should you choose?

Choose DIY AI Studio if you want a quick, controllable restoration and are prepared to keep the strength conservative. Choose MyHeritage for genealogy workflows and group photos where face-by-face inspection is useful. Choose Remini when blur and facial clarity are the main problem, but audit reconstructed details carefully. Choose VanceAI or Fotor for batches of visibly scratched and faded scans. Choose Photoshop when preserving the original evidence matters more than speed.

The decision rule is simple: a restoration has failed if it makes the photograph prettier by changing who or what was there. For family photos, fidelity beats sharpness.

FAQs

Which AI tool can restore an old family photo without changing the face?

No generative restoration tool can guarantee it will recover missing facial detail exactly. The safest approach is a conservative workflow: use gentle settings, compare facial landmarks against the source and reject any result that changes expression, eye shape, teeth, hairline or other identity cues. Photoshop offers the most manual control; DIY AI Studio is simpler when you want adjustable restoration strength.

Can AI remove scratches from old photos accurately?

AI is generally better at scratches, dust and creases when surrounding image information is still available. Risk rises when damage crosses a face, piece of jewellery, uniform detail or text because the model may need to invent what sits underneath.

Should I colourise an old black-and-white photo?

Colourisation can make a photograph easier to engage with, but treat it as an interpretation. Keep the restored black-and-white version as the primary record and save the colourised version separately.

Can AI accurately restore a severely damaged photo?

Only up to the point where enough source information remains. If a large part of a face, object or scene is missing, AI can reconstruct a plausible replacement but cannot prove that the invented detail matches the original. At that point, label the job as reconstruction rather than restoration.