TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Attention-based for Multiscale Fusion Underwater Image Enhancement |
| 영문제목(English Title) |
Attention-based for Multiscale Fusion Underwater Image Enhancement |
| 저자(Author) |
Zhixiong Huang
Jinjiang Li
Zhen Hua
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| 원문수록처(Citation) |
VOL 16 NO. 02 PP. 0544 ~ 0564 (2022. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the twoprocesses: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.
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| 키워드(Keyword) |
Underwater image enhancement
Multiscale fusion
Convolutional neural network
Attention mechanism
Local binary pattern
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| 파일첨부 |
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