TL;DR
Experiments show that the proposed binary denoising framework MSS-PBC achieves the best restoration performance among the evaluated binary methods, demonstrating that the proposed framework effectively narrows the restoration quality gap between binary and full-precision raw video denoising models.
Low-light image restoration remains a challenging problem due to poor illumination, colour distortion, structural detail loss, and amplified sensor noise. Existing restoration methods often rely on multi-stage enhancement and denoising pipelines that increase computational complexity and introduce error propagation dur...
Low-light image enhancement aims to improve image quality under insufficient illumination while recovering underlying structural and texture information. Existing approaches, ranging from conventional image processing techniques to deep generative models, have shown promising performance in brightness enhancement and d...
Low-light image enhancement is challenging due to noise and poor visibility. While diffusion models have shown remarkable generative capabilities, applying them directly to this task in the pixel domain remains problematic. They tend to struggle with the trade-off between global illumination correction and local detail...
Low-light image enhancement is challenging due to the strong entanglement between illumination degradation and sensor noise in RGB space, which often causes noise amplification and color distortion. We first present region-aware quantitative evidence that a luminance–chrominance transform provides a more separable degr...
Real-world video denoising for communication systems must handle mixed degradations such as sensor noise, compression artifacts, and motion-induced inconsistency while remaining deployable under tight latency budgets. Purely deep models often achieve high accuracy but come with significant computational cost, whereas c...
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