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.

通过引入新颖的亮度和色度双分支网络 (LCDBNet)，将低光图像增强分为亮度调整和色度恢复两个子任务，并使用多级小波分解学习细节敏感的特征，与其他先进算法相比，在多个参考和非参考质量评估器上取得了卓越性能。