How AI Old Photo Restoration Actually Works: From Pixel Interpolation to Generative Reconstruction

Every family has at least one photo like this: a grandparent’s wedding portrait with a crease running through a face, or a childhood snapshot so faded and scratched it barely reads as a photo anymore. For decades, fixing these was purely manual, skilled work. Today it’s something you can do in a browser in seconds. The gap between those two worlds is worth understanding, because “AI restoration” isn’t one trick — it’s actually a fairly recent, genuinely different approach to the problem than everything that came before it.

The old way: retouching and interpolation

Before generative AI, “digital restoration” meant one of two things. The high-end version was a skilled retoucher manually painting over damage in Photoshop, pixel by pixel, cloning texture from elsewhere in the same photo to cover a scratch — real craft, but slow and expensive, often taking hours per photo.

The automated version was much cruder: basic filters and interpolation. Interpolation, in this context, means estimating a missing or damaged pixel’s value by blending the values of the pixels around it — essentially averaging your way across a gap. This works fine for small blemishes like dust specks, but it fundamentally cannot recover detail that isn’t there. Interpolate across a two-centimeter crease running through someone’s eye, and you don’t get an eye back — you get a smooth, blurry smudge, because averaging nearby pixels is all the math can do. It has no concept of “this should probably be an eyelash” — it just sees numbers on either side of a gap and fills the middle with something in between.

The new way: generative reconstruction

Modern AI restoration works completely differently, because it isn’t just processing the damaged photo in isolation — it’s applying knowledge learned from an enormous number of other photos. A restoration model is trained on millions of real images: portraits, landscapes, fabric, skin, hair, in every lighting condition and every state of wear. Through that training, it builds an internal statistical understanding of what these things generally look like — what skin texture looks like at various ages and lighting angles, how fabric weave typically behaves, how hair strands catch light.

When that model encounters a damaged region in your photo, it isn’t averaging the surrounding pixels — it’s asking a much richer question: “given everything I know about what faces, skin, and fabric generally look like, and given the surrounding undamaged context in this specific photo, what’s the most plausible content that belongs here?” It then synthesizes new pixel detail consistent with both its general training and the specific photo’s local context — texture that looks like real skin or real fabric, not a blurred average of nearby pixels.

Two separate problems: damage repair and colorization

It helps to treat old-photo restoration as two distinct technical problems, because they’re solved with related but separate mechanisms.

Physical damage — scratches, creases, torn or missing chunks — is an inpainting problem. The model is effectively told (explicitly or by learning to detect damage patterns itself): “this region is corrupted, ignore it, and generate new content that’s consistent with what surrounds it.” This is the same family of technique used for removing unwanted objects or watermarks from images — a masked region gets filled in with plausible, context-consistent content rather than blended from nearby pixels.

Colorizing a black-and-white photo is a different kind of prediction problem. Here the model has full detail — nothing is missing structurally, only color information. It’s trained on huge numbers of real color photographs to learn the statistical relationship between grayscale tone/texture patterns and the colors those patterns are usually associated with: skin falls within certain predictable ranges, sky is typically some shade of blue or grey, foliage is typically green, wood is typically brown. Given a black-and-white input, the model predicts the most statistically plausible color for each region based on everything it learned about how the real world tends to be colored.

Being honest about what this actually is

It’s worth being direct about a limitation here, because it’s easy for “AI restoration” marketing to blur the line: this is plausible reconstruction, not literal recovery of lost information. When a scratch destroys the pixels where someone’s eye used to be, the physical light information that eye once reflected into a camera lens is gone forever — no algorithm, however sophisticated, can retrieve data that was never captured or was physically destroyed. What a generative model does instead is produce a highly informed, statistically grounded guess at what was probably there, based on the overwhelming majority of similar photos it learned from. For most everyday family photos, that guess is convincing enough to look completely natural — but it is inference, not archaeology. The same honesty applies to colorization: the model is predicting plausible color, not recovering the literal color that was actually in front of the camera the day the photo was taken.

Where this fits

This exact combination — generative inpainting for physical damage and learned color prediction for black-and-white photos — is what Memoria AI is built around. It’s free, requires no signup, and handles scratch repair and colorization directly in your browser, so a photo like that creased wedding portrait can go from damaged to genuinely presentable in the time it takes to upload it.