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.

翻译：本文提出IllumFlow，一种将条件整流流与Retinex理论协同融合的新型低光图像增强框架。该模型通过分别优化照明分量与反射分量来处理低光增强问题，有效应对光照变化与噪声干扰。具体而言，我们首先依据Retinex理论将输入图像分解为反射分量与照明分量。为建模低光图像中照明变化的宽动态范围，我们提出条件整流流框架，将照明变化表征为连续流场。复杂噪声主要存在于反射分量中，为此我们引入基于流场数据增强的降噪网络，在保持色彩保真度的同时消除反射噪声与色差。IllumFlow能够实现跨光照条件的精确照明自适应，并天然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，本方法在定量指标与视觉质量上均优于现有方法。