Underwater Image Enhancement Based on Multiscale Generative Adversarial Network

To address problems associated with capturing underwater images, i.e., blur details and color distortion caused by the absorption and scattering of light, an underwater image enhancement algorithm based on multiscale generative adversarial network is proposed. This algorithm uses an adversarial network as the basic framework, combining residual connections and dense connections to strengthen the propagation of underwater image features. First, the visual information in different spaces of a degraded image is extracted through two parallel branches, and a dense residual block is added to each branch to learn deeper features. Then, the features extracted from the two branches are fused and the detailed information of the image is restored through a reconstruction module. Finally, multiple loss functions are constructed and the adversarial network is repeatedly trained to obtain enhanced underwater images. The experimental results demonstrate that an underwater image enhanced using the algorithm has brighter colors and better dehazing effect. Compared with the original image, the average quality of the underwater color image is increased by 0. 1887; compared with the underwater residual network algorithm, the number of matching points of the speeded up robust features is increased by 17.