Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.

翻译：低光照图像增强旨在提升对比度、调整可见度并修复色彩与纹理失真。现有方法通常更关注通过增强低光照图像的亮度来改善可见度和对比度，而忽视了色彩与纹理修复对高质量图像的重要性。针对该问题，我们提出一种新型亮度-色度双分支网络LCDBNet，将低光照图像增强分解为亮度调整与色度修复两个子任务。具体而言，LCDBNet由亮度调整网络（LAN）和色度修复网络（CRN）两个分支构成。LAN利用长程依赖与局部注意力相关性学习亮度感知特征，CRN则通过多级小波分解专注于敏感细节特征学习。最后设计融合网络将两分支学习特征进行混合，生成视觉惊艳的图像。在七个基准数据集上的大量实验验证了所提LCDBNet的有效性，结果表明相较其他先进方法，LCDBNet在多种参考/无参考质量评估指标上均取得更优性能。相关代码与预训练模型将开源。