Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement

Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement

Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an illposed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-bas...

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Bibliographic Details
Published in:2022 IEEE International Conference on Image Processing (ICIP) pp. 3878 - 3882
Main Authors: Fan, Chi-Mao, Liu, Tsung-Jung, Liu, Kuan-Hsien
Format: Conference Proceeding
Language:English
Published: IEEE 16-10-2022
Subjects:
attention mechanism
Computer vision
hierarchical
Image enhancement
Image segmentation
M-Net
Measurement
Neural networks
Semantics
Visualization
Wavelet domain
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Summary:Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an illposed 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.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897503