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- W4386690139 abstract "Abstract Underwater image enhancement is a technique that improves the quality of underwater images, which makes them clearer and more realistic. However, because of the complexity of underwater environments, underwater image enhancement faces many challenges, such as the variation in underwater optical properties as well as low contrast, low brightness, and color distortion in underwater images. To extract underwater image features more effectively, this paper proposes an underwater image enhancement algorithm called Cross Attention-based Underwater Image Enhancement (CAUIE). The algorithm combines cross attention and dynamic enhancement modules to build a U-Net model. Cross attention uses a self-attention mechanism to capture the local and global information of underwater images, thus enhancing the semantic representation of the images. The dynamic enhancement module, by contrast, dynamically adjusts the enhancement parameters according to different regions of the image to acquire detail information. In addition, this paper introduces a contrastive regularization loss to construct a hybrid loss function for guiding the training and optimization of the model. The experimental results show that the proposed algorithm outperforms the comparison algorithm in both subjective visual and objective evaluation criteria. Moreover, the proposed model obtains PSNR and SSIM results of 34.86 dB and 0.996, respectively, increasing the results of the previous model by 7.97 dB and 0.099, which illustrates that the proposed algorithm can solve the color distortion problem and recover the contrast and clarity of underwater images." @default.
- W4386690139 created "2023-09-13" @default.
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- W4386690139 date "2023-09-12" @default.
- W4386690139 modified "2023-10-15" @default.
- W4386690139 title "Underwater image enhancement method based on a cross attention mechanism" @default.
- W4386690139 doi "https://doi.org/10.21203/rs.3.rs-3285291/v1" @default.
- W4386690139 hasPublicationYear "2023" @default.
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