Underwater Image Enhancement Method Based on Multi-scale Cascade Network

MI Zetian; JIN Jie; LI Yuanyuan; DING Xueyan; LIANG Zheng; FU Xianping

doi:10.11999/JEIT220375

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3353-3362

MI Zetian, JIN Jie, LI Yuanyuan, DING Xueyan, LIANG Zheng, FU Xianping. Underwater Image Enhancement Method Based on Multi-scale Cascade Network[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3353-3362. doi: 10.11999/JEIT220375

Citation:

MI Zetian, JIN Jie, LI Yuanyuan, DING Xueyan, LIANG Zheng, FU Xianping. Underwater Image Enhancement Method Based on Multi-scale Cascade Network[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3353-3362. doi: 10.11999/JEIT220375

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Underwater Image Enhancement Method Based on Multi-scale Cascade Network

doi: 10.11999/JEIT220375

1.
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2.
Peng Cheng Laboratory, Shenzhen 518000, China
3.
College of Internet, Anhui University, Hefei 230039, China

Funds: The National Natural Science Foundation of China (62176037), The Foundation of Liaoning Province Key Research and Development Program (201801728)

Received Date: 2022-04-01
Rev Recd Date: 2022-06-23

Available Online: 2022-06-29

Publish Date: 2022-10-19

Abstract

Abstract

Focusing on the serious color shift and loss of details caused by light absorption, backscattering and other factors in underwater images, an underwater image enhancement method based on multi-scale cascaded network is proposed in this paper. For the image gradient dissipation caused by incomplete utilization of features via single network, better details are preserved by cascading multi-scale original images and corresponding feature images, and rapid prediction of residuals from shallower layers to deeper layers can be realized at the same time. In addition, joint dense network block and recursive block are introduced to avoid effectively the problem of excessive parameters introduced by conventional multi-scale network through feature reuse. A joint loss function of Charbonnier and the Structural SIMilarity (SSIM) is proposed to solve effectively the problem of uneven restoration of image details caused by a single loss. The simulation experiments show that the proposed network has achieved excellent results in dealing with severe color shift and loss of details.
- Underwater image enhancement,
- Multi-scale cascade network,
- Multi-scale feature extraction,
- Gradient disappears

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References(29)

References

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