IllumFlow：基于条件修正流和Retinex分解的光照自适应低照度增强
IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition
AI总结：
提出IllumFlow框架，结合条件修正流与Retinex理论分离优化光照与反射分量，实现精确光照适应、去噪和可定制亮度增强，在低照度增强和曝光校正任务上优于现有方法。
摘要：
我们提出了IllumFlow，这是一个将条件修正流（CRF）与Retinex理论协同用于低照度图像增强（LLIE）的新颖框架。我们的模型通过对光照和反射分量进行分离优化来解决低照度增强问题，有效处理光照变化和噪声。具体而言，我们首先遵循Retinex理论将输入图像分解为反射分量和光照分量。为了对低照度图像中光照变化的宽动态范围进行建模，我们提出了一个条件修正流框架，将光照变化表示为连续流场。虽然复杂噪声主要存在于反射分量中，我们引入了一个由流衍生数据增强增强的去噪网络，以在保持颜色保真度的同时去除反射噪声和色差。IllumFlow实现了跨光照条件的精确光照适应，同时自然支持可定制的亮度增强。在低照度增强和曝光校正上的大量实验表明，其定量和定性性能均优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
