通过光照自适应流模型，实现低光图像的精准亮度调节与降噪。
IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition
- 分离光照与反射分量，用条件修正流建模光照变化。
- 在多个数据集上显著优于现有方法，有效提升亮度与色彩保真度。
- 适合需要高精度低光增强的应用场景，如夜视成像、手机摄影。
我们提出IllumFlow，一种结合条件修正流（CRF）与Retinex理论的低光图像增强新框架。该模型通过分离反射率与光照分量，分别优化以应对光照变化和噪声。基于Retinex理论，将输入图像分解为反射率与光照成分；针对低光图像中光照动态范围广的问题，设计条件修正流框架，将光照变化建模为连续流场。由于复杂噪声主要集中在反射率成分，我们引入去噪网络，并结合流生成的数据增强策略，在保留色彩真实性的前提下有效去除反射率噪声与色差。IllumFlow可在不同光照条件下实现精确的光照自适应，同时支持可定制的亮度增强。在低光增强与曝光校正任务上的大量实验表明，其定量与定性表现均优于现有方法。
原文摘要 · Abstract (English)
We present IllumFlow, a novel framework that synergizes conditional Rectified Flow (CRF) with Retinex theory for low-light image enhancement (LLIE). Our model addresses low-light enhancement through separate optimization of illumination and reflectance components, effectively handling both lighting variations and noise. Specifically, we first decompose an input image into reflectance and illumination components following Retinex theory. To model the wide dynamic range of illumination variations in low-light images, we propose a conditional rectified flow framework that represents illumination changes as a continuous flow field. While complex noise primarily resides in the reflectance component, we introduce a denoising network, enhanced by flow-derived data augmentation, to remove reflectance noise and chromatic aberration while preserving color fidelity. IllumFlow enables precise illumination adaptation across lighting conditions while naturally supporting customizable brightness enhancement. Extensive experiments on low-light enhancement and exposure correction demonstrate superior quantitative and qualitative performance over existing methods.
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