Swin transformer and fusion for underwater image enhancement

Abstract

In an underwater scene, refraction, absorption, and scattering of light by suspended particles in water degrade the visibility of images, causing low contrast, blurred details and color distortion. Based on the characteristics of underwater image degradation, we proposed a fusion neural network, which builds on the blending of two images that are derived from a white-balanced and color-compensated version of the raw underwater image. The two images are fused through the image enhancement module. In the previous works, convolutional neural networks (CNN) have been widely used in underwater image enhancement tasks. However, the local computational characteristics of convolutional operations limit the effect of the image enhancement. Recently, transformers have shown impressive performance on low-level vision tasks. In this paper, we propose a module SwinMT for image enhancement based on the swin transformer. First, we generate two inputs by respectively applying white balance (WB) and gamma correction (GC) algorithms to an underwater image. Second, the SwinMT module extracts features respectively, which consists of two parts: low-frequency feature extraction module and high-frequency feature to restore high-quality. We conduct experiments on rendered synthetic underwater images. Experiments on underwater images show that our method produces visually pleasing results, and we compare results with state-of-the-art techniques.