Low-light image enhancement tasks demand an appropriate balance among brightness, color, and illumination. While existing methods often focus on one aspect of the image without considering how to pay attention to this balance, which will cause problems of color distortion and overexposure etc. This seriously affects both human visual perception and the performance of high-level visual models. In this work, a novel synergistic structure is proposed which can balance brightness, color, and illumination more effectively. Specifically, the proposed method, so-called Joint Correcting and Refinement Network (JCRNet), which mainly consists of three stages to balance brightness, color, and illumination of enhancement. Stage 1: we utilize a basic encoder-decoder and local supervision mechanism to extract local information and more comprehensive details for enhancement. Stage 2: cross-stage feature transmission and spatial feature transformation further facilitate color correction and feature refinement. Stage 3: we employ a dynamic illumination adjustment approach to embed residuals between predicted and ground truth images into the model, adaptively adjusting illumination balance. Extensive experiments demonstrate that the proposed method exhibits comprehensive performance advantages over 21 state-of-the-art methods on 9 benchmark datasets. Furthermore, a more persuasive experiment has been conducted to validate our approach the effectiveness in downstream visual tasks (e.g., saliency detection). Compared to several enhancement models, the proposed method effectively improves the segmentation results and quantitative metrics of saliency detection. The source code will be available at https://github.com/woshiyll/JCRNet.

翻译：低光照图像增强任务需要在亮度、色彩与光照之间实现适当平衡。现有方法往往仅关注图像的单一维度，而忽略如何维持这种平衡性，导致颜色失真、过曝光等问题。这严重影响了人类视觉感知效果与高层视觉模型的性能表现。本文提出一种新型协同结构，能够更有效地平衡亮度、色彩与光照。具体而言，所提出的联合校正与细化网络（JCRNet）主要包含三个阶段：第一阶段：利用基础编码器-解码器与局部监督机制提取局部信息及更全面的细节用于增强；第二阶段：跨阶段特征传递与空间特征变换进一步促进色彩校正与特征细化；第三阶段：采用动态光照调整方法，将预测图像与真实图像之间的残差嵌入模型，自适应调整光照平衡。大量实验表明，该方法在9个基准数据集上对21种最新方法展现出全面的性能优势。此外，更具说服力的下游视觉任务（如显著性检测）验证了方法的有效性。相比多种增强模型，所提方法有效提升了分割结果与显著性检测的量化指标。源代码将在https://github.com/woshiyll/JCRNet 公开。