Underwater image enhancement is a key technology for underwater missions.Aiming at the problems of color distor-tion and image blurring in underwater images,this paper designs an underwater domain adaptation network(UDA Net)based on the domain adaptive method to achieve effective enhancement of underwater images under unsupervised conditions.The sharp-ness of the original underwater images is significantly improved.Based on U-Net network framework,the algorithm uses convolu-tional neural network and multi-head attention mechanism for feature extraction,introduces adversarial learning idea,and adds discrimination network to domain feature extraction module and output module.Meanwhile,it optimizes feature enhancement loss,feature alignment loss and output alignment loss in the source domain to ensure style transfer and feature alignment from the source domain to the target domain,achieving underwater enhancement.In addition,the public underwater data sets EUVP,UIEB and UFO-120 are used for experimental verification,and the experimental results are compared with cutting-edge en-hancement algorithms.The effectiveness of UDA Net algorithm is proved,and it has a good application prospect in underwater image enhancement tasks.
underwater image enhancementstyle transferadversarial learningloss function
万方数据
维普
