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<p>Traditional dark channel restoration methods can improve image quality to a certain extent, but they are limited by the accuracy of parameter estimation and cannot completely eliminate degradation phenomena. In this paper, we propose a dual-branch super-resolution residual network for underwater image restoration. The network consists of a preprocessing module, a dual-branch residual module, and a super-resolution module. By introducing the preprocessing module, the network ensures the authenticity of the restored scene while achieving a two-level enhancement. The dual-branch residual module incorporates a high-frequency branch and a CBAM attention mechanism to enhance the network's ability to extract high-frequency features from images. During training, to improve the overall restoration performance of the network, a data degradation strategy is employed to increase the diversity among data. A joint loss function combining mean squared error loss, structural similarity loss, and perceptual loss is utilized to enhance the network's sensitivity to feature differences. Experimental results on the test set demonstrate that the proposed method outperforms other restoration methods, with UIQM and UCIQE metrics reaching 1.718 and 0.478, respectively.</p>

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Underwater image restoration based on a dual-branch super-resolution residual network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月9日

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  • Daoping Du,
  • Lanlan Pan,
  • Ye Liang,
  • Honghao Yang,
  • Xinyu Sui,
  • Xiang Li

摘要

Traditional dark channel restoration methods can improve image quality to a certain extent, but they are limited by the accuracy of parameter estimation and cannot completely eliminate degradation phenomena. In this paper, we propose a dual-branch super-resolution residual network for underwater image restoration. The network consists of a preprocessing module, a dual-branch residual module, and a super-resolution module. By introducing the preprocessing module, the network ensures the authenticity of the restored scene while achieving a two-level enhancement. The dual-branch residual module incorporates a high-frequency branch and a CBAM attention mechanism to enhance the network's ability to extract high-frequency features from images. During training, to improve the overall restoration performance of the network, a data degradation strategy is employed to increase the diversity among data. A joint loss function combining mean squared error loss, structural similarity loss, and perceptual loss is utilized to enhance the network's sensitivity to feature differences. Experimental results on the test set demonstrate that the proposed method outperforms other restoration methods, with UIQM and UCIQE metrics reaching 1.718 and 0.478, respectively.