Abstract: Aiming at the problem of color cast and blur in the process of acquisition and transmission of underwater images, we proposed an enhancement method of underwater color cast image based on deep convolutional neural network, and gave the convergence analysis of the proposed method. Firstly, on the basis of the traditional U-Net model, a convolutional neural network model based on color cast images was constructed to continuously learn the color cast between the input image and the output image. Secondly, by using the loss function of structural similarity, the enhanced underwater image was highly similar to the input underwater image in the details of the content structure. The model solved the problem of color cast and distortion of underwater images. By verifying a large number of real underwater data sets, and compared with other algorithms, the experimental results show that the underwater image enhanced by the method not only improves the visual effect effectively, but also retains the texture structure contained in the original image, which proves that the model has high practicability in the field of underwater image enhancement.

Key words: underwater image optimization, image color enhancement, convolutional neural network, structural similarity