Optimized UNet framework with a joint loss function for underwater image enhancement.

As the water economy advances and the concepts of water ecology protection and sustainable development take root in people's minds, underwater imaging equipment has made remarkable progress. However, due to various factors, underwater images still suffer from low quality. How to enhance the quality of underwater images so that people can understand them quickly has become a crucial issue. Therefore, aiming at the degradation problems such as detail blurring, color imbalance, and noise interference in low-quality underwater images, this paper proposes an optimized UNet framework with a joint loss function (OUNet-JL). Firstly, to alleviate the problem of detail blurring, we construct a multi-residual module (MRM) to enhance the ability to represent detail features by using serially stacked convolutional blocks and residual connections. Secondly, we build a spatial multi-scale feature extraction module fused with channel attention (SMFM) to address the color imbalance issue through multi-scale dilated convolution and channel attention. Thirdly, to improve the signal-to-noise ratio of the enhanced image and solve the problem of blurring distortion, a strengthen-operate-subtract feature reconstruction module (SOSFM) is presented. Fourthly, to guide the network to perform training more efficiently and help it converge rapidly, a joint loss function is designed by integrating four different loss functions. Extensive experiments conducted on the well-known UIEB and UFO-120 datasets have shown the superiority of our OUNet-JL compared with several state-of-the-art algorithms. Moreover, ablation studies have also verified the effectiveness of the proposed modules. Our source code is publicly available at https://github.com/WangXin81/OUNet_JL .