Samsung · AI / ML · Filed Apr 13, 2026 · USPTO published Nov 12, 2026 · This week's release

Samsung Files Patent for Training AI to Restore Fine Detail in Blurry Photos

Zoom into an old photo and it falls apart into mush. Samsung has filed a patent on a training method meant to help AI rebuild the fine texture that low-resolution images lose.

By Patentlyze Team · Updated Nov 12, 2026 · 3 min read

A blurry photo of a patterned cushion alongside sharp, reconstructed versions generated with artificial intelligence. Drawing from patent filing US 2026/0359958 A1. — A blurry photo of a patterned cushion alongside sharp, reconstructed versions generated with artificial intelligence.All 8 drawings ↓

Publication number: US 2026/0359958 A1(US20260359958A1)
Applicant: Samsung Electronics Co., Ltd.
Filed · Published: Apr 13, 2026 · Nov 12, 2026
Inventors: Joonsoo Kim, Tien C. Bau
Status when we published: Waiting for an examiner
Document: 20 claims
US classification: 382/274
Parent application: Claims priority from a provisional application 63802452 (filed May 8, 2025)

AI training & infrastructure

What Samsung's photo-sharpening AI actually does

A photo taken years ago on an old phone looks soft the moment you zoom in. A show made for a smaller screen looks the same when it gets stretched across a big TV.

Samsung's patent is about teaching an AI to fill in the missing fine detail, like the leaves of a plant or the fabric of a couch pillow, instead of just making the blur bigger. The AI starts from pure static and cleans it up step by step until a sharper picture appears, guided by the small original image.

The new part is how the AI is graded while it learns. Instead of only checking whether it removed the static correctly, it also gets scored on whether the finished picture looks right to a separate image-checking system. That extra score is meant to push it toward sharper, cleaner detail.

From the filing · CLAIM 1

generating a super resolution (SR) image using an artificial intelligence model to restore perceptual details of an image, wherein the artificial intelligence model is trained using perceptual loss …

Translation: An AI sharpens blurry pictures by learning how to fill in missing visual details.

How the AI is graded while it learns detail

The patent covers super resolution (turning a small, low-resolution picture into a larger, sharper one) using a diffusion model, an AI that builds an image by starting from random static and removing noise in many small steps. The claims focus on how it is trained, using a perceptual loss, a score for whether a result looks right to the eye rather than whether each pixel matches.

Training works like this:

The AI works on the residual, the difference between the sharp and blurry versions, instead of rebuilding the whole picture.
It predicts the noise in a noisy residual, and that is combined with the low-resolution image to form a predicted sharp image. The same is done with the true noise to form a reference image.
Both images go through a perceptual network (the filing says it can be a standard image-analysis model called VGG), which compares them and produces the score.

The loss function weights two scores differently at different steps. Very noisy steps lean on the plain noise-matching score, since image-checking networks cannot read heavy static. Cleaner steps lean more on the perceptual score. The filing says keeping the low-resolution image in every prediction stops training from falling apart.

The filing adds that smaller networks with fewer steps lose detail, and this training helps them keep it.

From the filing · THE ABSTRACT

… the perceptual loss is calculated using a predicted denoised residual image, an original denoised residual image, and a low-resolution (LR) image.

Translation: The system trains itself by comparing the cleaned up photo against the original blurry image.

Why this matters for TVs and phone photos

If this works as described, photos you zoom into and video stretched onto a big screen could keep more fine detail. The filing names smartphones and televisions as places it could be used, and says the method is especially helpful when the AI is made smaller and runs in fewer steps. Smaller models are easier to fit on everyday devices.

For Samsung, which makes both phones and TVs, better upscaling is a visible quality feature. The filing includes two before-and-after image sets, a plant and a couch pillow, showing sharper and cleaner results with the perceptual score than without it.

Samsung's 168th filing we've tracked since May builds on ideas like battery-aware photo cleanup and restoring zoomed detail, adding another piece to our camera sensor push watchlist.

Editorial take

Every sharpening trick makes a bet. The AI is adding detail, and detail it adds is a best guess about what was there. Samsung's training tweak pushes that guess toward crisper results, which looks great on a leaf or a couch pillow and could look wrong on a face or a street sign.

The engineering cost is a heavier training process. The AI needs a second image-checking system running at every step, plus careful balancing between two scores. The filing says that balancing is what keeps training from falling apart, so the added work buys stability. The trade reads as worth it, since the extra checking only happens during training.

The evidence in the filing is two image sets with no measured numbers. Read it as a sensible training recipe with modest proof so far.

Source: US 2026/0359958 A1 at the USPTO (PDF). Full patent text and figures are in the official publication.

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