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。

改进的分割：学习适应暗光图像增强的亮度感知和细节敏感表示

Division Gets Better: Learning Brightness-Aware and Detail-Sensitive  Representations for Low-Light Image Enhancement

We propose a novel method for adjusting luminance for multi-exposure image
fusion. For the adjustment, two novel scene segmentation approaches based on
luminance distribution are also proposed. Multi-exposure image fusion is a
method for producing images that are expected to be more informative and
perceptually appealing than any of the input ones, by directly fusing photos
taken with different exposures. However, existing fusion methods often produce
unclear fused images when input images do not have a sufficient number of
different exposure levels. In this paper, we point out that adjusting the
luminance of input images makes it possible to improve the quality of the final
fused images. This insight is the basis of the proposed method. The proposed
method enables us to produce high-quality images, even when undesirable inputs
are given. Visual comparison results show that the proposed method can produce
images that clearly represent a whole scene. In addition, multi-exposure image
fusion with the proposed method outperforms state-of-the-art fusion methods in
terms of MEF-SSIM, discrete entropy, tone mapped image quality index, and
statistical naturalness.

本文提出了一种新颖的多曝光图像融合的亮度调整方法，该方法基于亮度分布提出了两种新颖的场景分割方法。实验结果表明，该方法可以在给定不理想输入时生成高质量的图像，并且在多个评测指标上优于其他最先进的方法。