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Depthwise Separable Residual Dual-attention GAN for Underwater Image Enhancement

Published: 14 June 2024 Publication History

Abstract

Due to the complex underwater environment, underwater images often show low contrast, detail loss, color distortion and other problems, which have a certain impact on underwater operations. In order to overcome the above problems and improve the underwater image quality, a depthwise separable residual dual-attention GAN for underwater image enhancement model is proposed in this paper. In the proposed model, a dual-attention module is designed to better preserve the high frequency information of the image. And a depthwise separable residual module is designed to reduce the semantic difference between the encoder and the decoder. Then, the enhanced underwater image is obtained by the training of multiple joint losses. Finally, comparing with related models, the performance of the proposed model is verified by experiments on EUVP dataset and UIEB dataset. The experiment results show that in terms of visual effects and quantitative metrics, the proposed model can achieve better brightness contrast, detail preservation and color recovery for the enhanced underwater images.

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AIPR '23: Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 14 June 2024

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Author Tags

  1. Depthwise separable residual module
  2. Dual-attention module
  3. Generative adversarial network
  4. Underwater image enhancement

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