MeanFlow framework enhances low-light RAW images with motion blur

Researchers have introduced MeanFlow, a novel framework designed to enhance extremely low-light RAW images that also suffer from motion blur. This approach addresses the common oversight in existing methods that focus on illumination and noise while neglecting motion degradation. The framework includes a new dataset, SIDED, which captures controlled motion blur in low-light RAW images, and a unified RAW tokenizer to align different lighting conditions. MeanFlow performs enhancement in a single function evaluation, and a physics-guided refinement model further improves image quality without increasing computational cost. AI

IMPACT Introduces a new method for enhancing low-light images with motion blur, potentially improving performance in challenging photographic conditions.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel framework for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]

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