Aiming at the problems of serious color cast and blurring of images in underwater environment

this paper proposes a novel generative adversarial network algorithm. U-Net is used as the basic model of the generative network and improved. Firstly

the attention mechanism is introduced into the network

and a multi-scale feature extraction module is designed to extract features at different levels. Secondly

the robustness of the model is improved by preprocessing the input white balance image. In order to solve the problem of uneven restoration of image details caused by a single loss

L1 loss and content loss are combined in the traditional adversarial loss function. The experimental results show that this method has a good effect on color recovery and sharpness improvement of underwater images

where the average value of structural similarity

peak signal-to-noise ratio

underwater color quality assessment

and underwater image quality metric is 0.8906

29.0761

0.4454 and 3.1810 respectively. In terms of subjective evaluation and objective evaluation indicators

the experimental results of the algorithm in this paper are better than the comparison algorithms.