Dual attention fusion generative adversarial network for underwater image enhancement

In order to solve the problem that underwater image will be polluted by underwater impurities and absorbed by light in the process of generation, an underwater image enhancement algorithm based on dual attention fusion generative adversarial network was proposed. The algorithm uses the latest Pix2Pix network architecture and constructs a dual attention mechanism structure to create rich context information to process underwater images.At the head of the model generator UNet network, an improved Non local module is added to obtain more global features from a multi-scale perspective, so as to get a clearer image. At the end of the generator, a Transformer module is introduced to improve the comprehensive performance of the model through its excellent multi head attention block and multi-layer perceptron and other structures, so as to further improve the model Semantic information extraction capability. The experimental results show that the peak signal-to-noise ratio, structural similarity, and underwater image quality evaluation indicators of the model on the benchmark dataset EUVP have increased by an average of 5.83%, 4.88%, and 18.02% compared to other models, while the corresponding indicators on the benchmark dataset EUVP have increased by an average of 6.21%, 17.33%, and 15.96%. Under the subjective visualization results, the model can also properly deal with the problem of image degradation, so that the image presents better clarity and contrast.