Deblur.
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Motion blur, removed.

Full resolution in, uncompressed out.

Restored photo Original photo
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When your hand moves while the shutter is open, every point of light is recorded as a short streak instead of a point. Sharpening cannot put that back, it only raises contrast on whatever survived. This uses a neural network trained on 7,400 pairs of the same scene, captured once shaken and once still, so it has learned what the smeared detail is supposed to look like and reconstructs it.

The pairs all come from one phone, so photos from other cameras, shots that were out of focus rather than shaken, and very dark scenes may not improve.

Research

Paper iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring Abdullah Al Shafi, Kazi Saeed Alam. arXiv, 2026. Dataset iPhoneBlur 7,400 real blur and sharp pairs from hand-held video, stratified into easy, medium and hard. Model Fine-tuned restoration weights The checkpoint behind this page, with the other baselines from the paper. Code Benchmark and training code Dataset construction, difficulty stratification and evaluation.
Abdullah Al Shafi
Abdullah Al Shafi

Computer vision, generative models, medical image analysis

Photos are processed in memory and never stored.