Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization; then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features; finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects.

中文翻译：

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eep监督残留稠密网络，用于水下图像增强

水下图像是水下信息的重要载体和形式，在探索和利用海洋资源方面发挥着至关重要的作用。然而，由于光的吸收和散射，水下图像具有低对比度和细节模糊的特征。近年来，深度学习由于其强大的特征学习能力而被广泛用于水下图像增强和还原，但是详细增强仍然存在缺陷。为了解决这个问题，本文提出了一种深度监督的残差密集网络（DS_RD_Net），该网络用于更好地了解清晰的空中图像与合成的水下退化图像之间的映射关系。DS_RD_Net首先使用残差密集块提取特征以提高特征利用率；然后，它在编码器和解码器之间添加了剩余路径块，以减少低级功能和高级功能之间的语义差异；最后，它采用了深度监督机制来指导网络训练，以改善梯度传播。实验结果（PSNR为36.2，SSIM为96.5％，UCIQE为0.53）表明，与其他图像增强方法相比，该方法可以完全保留图像的局部细节，同时进行色彩还原和除雾，达到了良好的定性和定量效果。