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Low-Light Image Enhancement with an Anti-Attention Block-Based Generative Adversarial Network
| Content Provider | MDPI |
|---|---|
| Author | MuWei, Jian Qiao, Junbo Wang, Xing Chen, Ji |
| Copyright Year | 2022 |
| Description | High-quality images are difficult to obtain in complex environments, such as underground or underwater. The low performance of images that are captured under low-light conditions significantly restricts the development of various engineering applications. However, existing algorithms exhibit color distortion or under/overexposure when addressing non-uniform illumination images. Furthermore, they introduce high-level noise when processing extremely dark images. In this paper, we propose a novel generative adversarial network (GAN) structure to generate high-quality enhanced images, which is called anti-attention block (AAB)-based generative adversarial networks (AABGAN). Specifically, we propose AAB to suppress undesired chromatic aberrations and establish a mapping relationship between different channels. The deep aggregation pyramid pooling module guides the network when combining multi-scale context information. Furthermore, we design a new multiple loss function to adjust images to the most suitable range for human vision. The results of extensive experiments show that our method outperforms state-of-the-art unsupervised image enhancement methods in terms of noise reduction and has a well-perceived result. |
| Starting Page | 1627 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11101627 |
| Journal | Electronics |
| Issue Number | 10 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-19 |
| Access Restriction | Open |
| Subject Keyword | Electronics Artificial Intelligence Industrial Engineering Low-light Image Enhancement Coal Image Processing Underwater Image Processing Generative Adversarial Network Deep Learning |
| Content Type | Text |
| Resource Type | Article |
