An underwater image enhancement method based on Swin transformer

Abstract

An underwater image enhancement model based on Swin Transformer is designed to solve the problems of color shift, low contrast, detail loss and noise in underwater images caused by absorption and scattering effects during the propagation of light underwater. The model consists of two parts: feature extraction and image reconstruction. First, image features are extracted using a set of improved residual Swin Transformer modules, which allows the model to process images with fewer parameters and establish long-term dependencies between image features. Further, a parallel attention mechanism is introduced into the model to focus on both semantic and spatial information contained in underwater images to improve image enhancement. Experimental results show that compared with existing popular underwater image enhancement methods, the proposed Underwater Image Enhancement Method in this paper has advantages in both the number of network parameters and image enhancement accuracy and has made significant progress in correcting color bias, improving contrast, enhancing details and eliminating noise.