Samsung Patents an ML Training Method for Fixing Blurry Handheld Photos

By Patentlyze Team · Updated May 22, 2026

Taking sharp photos in low light is hard — your hands shake, frames blur, and stacking multiple shots makes the misalignment worse. Samsung's new patent describes a way to train AI models to fix exactly that, by teaching them on artificially degraded images before they ever see a real shaky shot.

What Samsung's motion-blur AI training actually does

Imagine you're shooting a candlelit dinner on your phone. The camera grabs several frames quickly and tries to merge them into one sharp image — but your hand moved slightly between each shot, so the result can look smeared or ghosted. Multi-frame blending is the tech trying to solve that, and it relies on AI models that need a lot of training data.

The problem is that collecting perfectly matched pairs of blurry real-world photos and their sharp originals is painfully slow. Samsung's patent proposes a shortcut: take clean, sharp training images you already have, then synthetically add realistic motion blur and warping to them in software. Now you have unlimited blurry-plus-sharp pairs to train on.

The result is an AI that learns to undo blur and realign frames — trained on fake shake, but capable of handling your real-world shaky hand. It's a data-augmentation trick that could mean sharper photos in challenging shooting conditions without needing to gather huge real-world datasets.

How Samsung simulates shake to train the blur-removal model

The patent describes a training pipeline for a multi-frame blending model — the kind of AI that takes several rapid-fire frames and merges them into a single high-quality image.

The core innovation is in data augmentation (the process of artificially expanding your training dataset). Rather than sourcing millions of genuinely blurry real photos paired with ground-truth sharp versions, the method:

• Starts with existing clean image sets that already have known ground-truth sharp references
• Programmatically applies simulated motion blur (replicating the smearing caused by camera movement during exposure) and warping (geometric distortion that mimics frame-to-frame misalignment) to generate degraded versions
• Uses those synthetically degraded frame sets alongside their ground-truth originals to train the ML model

The trained model learns two coupled tasks: frame alignment (figuring out how each captured frame has shifted relative to the others) and blur removal (deconvolving the motion smear from each frame). Crucially, the patent emphasizes that the simulated augmentations are designed to mimic handheld motion patterns specifically — not arbitrary random noise — which keeps the training distribution realistic.

What this means for Galaxy camera processing pipelines

For Samsung's Galaxy camera stack, which already leans heavily on computational photography, this kind of training shortcut could meaningfully lower the cost of improving low-light and burst photography models. Building large real-world blur datasets is expensive and slow; generating synthetic ones from existing clean image libraries is comparatively cheap and scalable. Better training data pipelines often matter more than model architecture changes when you're iterating fast across product generations.

For you as a phone photographer, the downstream effect is potentially sharper burst shots and less ghosting on moving subjects — especially in dim environments where multi-frame stacking is doing the heaviest lifting. It's not a flashy feature, but it's the kind of foundational work that quietly makes everything feel more polished.

Editorial commentary on a publicly published patent application. Not legal advice.