Hybrid attention triple branch transformer net for underwater image enhancement

Abstract

In real underwater scenes, the complexity of the environment leads to issues like light attenuation, scattering, and color distortion, resulting in reduced image quality and loss of details. To resolve these problems, we propose a hybrid attention triple branch transformer network (HATBformer). The backbone network adopts a three-layer encoder-decoder structure, making full use of the spatial and channel feature information of underwater images, and improving the network’s focus on color information and spatial regions with higher levels of attenuation. The detail enhancement branch incorporates the coordinate information perception mechanism and feature integration strategy through three consecutive feature enhancement blocks, aiming to deeply repair and optimize image details and effectively improve the image reconstruction quality. In addition, we established an underwater image dataset NLOS-TW that contains different optical thicknesses, including rich targets and various underwater scenes. Extensive experiments demonstrate that our method significantly enhances image quality and surpasses current state-of-the-art methods both qualitatively and quantitatively.