  Low-Light Image Enhancement is a computer vision task which intensifies the
dark images to appropriate brightness. It can also be seen as an ill-posed
problem in image restoration domain. With the success of deep neural networks,
the convolutional neural networks surpass the traditional algorithm-based
methods and become the mainstream in the computer vision area. To advance the
performance of enhancement algorithms, we propose an image enhancement network
(HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use
a half wavelet attention block on M-Net+ to enrich the features from wavelet
domain. Furthermore, our HWMNet has competitive performance results on two
image enhancement datasets in terms of quantitative metrics and visual quality.
The source code and pretrained model are available at
https://github.com/FanChiMao/HWMNet.
