当极端黑暗遭遇运动模糊：用于统一RAW修复的MeanFlow
When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration
AI总结：
本文针对现有低光RAW增强忽略运动退化的问题，提出含SIDED数据集、统一RAW分词器、MeanFlow及物理引导细化模型的框架，实现最优性能并稳健处理耦合退化。
AI中文摘要：
极端低光RAW增强旨在恢复严重衰减的传感器信号，但现有方法往往聚焦于光照与噪声，却忽略了实际低光成像中固有的运动诱导退化。本文提出一种针对实际采集退化的鲁棒极端低光RAW增强框架：首先，引入新数据集SIDED（在退化极端黑暗中观测），该数据集对极端低光RAW对施加可控运动退化，同时保留其原始传感器噪声；其次，提出配备显式域条件表示校准的统一RAW分词器，以对齐极端低光与曝光良好的RAW数据，随后采用MeanFlow通过单次函数评估完成增强。据所知，这是首个针对实际运动退化采集下的极端低光RAW增强问题，并用MeanFlow加以解决的工作。本文进一步引入物理引导的细化模型，以强化光照-反射一致性、像素保真度与色彩保留，且不增加额外推理成本。大量实验表明，该框架在极端低光RAW增强中达到了当前最优性能，且能稳健处理运动与噪声耦合退化。
英文摘要：
Extremely low-light RAW enhancement aims to recover severely attenuated sensor signals, yet existing methods often focus on illumination and noise while overlooking the motion-induced degradations inherent in practical low-light imaging. We present a framework for robust extremely low-light RAW enhancement under realistic acquisition degradations. First, we introduce See in the Degraded Extremely Dark (SIDED), a new dataset that applies controlled motion degradation to extremely low-light RAW pairs while retaining their original sensor noise. Second, we propose a unified RAW tokenizer equipped with explicit domain-conditioned representation calibration to align extremely low-light and well-exposed RAW data, followed by a MeanFlow that performs enhancement in a single function evaluation. To our knowledge, this is the first work to formulate extremely low-light RAW enhancement under realistic motion-degraded acquisition and address it with MeanFlow. We further introduce a physics-guided refinement model to strengthen illumination--reflectance consistency, pixel fidelity, and color preservation without incurring additional inference cost. Extensive experiments demonstrate that our framework achieves state-of-the-art performance in extremely low-light RAW enhancement, and robustly handles coupled motion and noise degradations.
