UDAformer: Underwater image enhancement based on dual attention transformer

Abstract

Underwater images suffer from color casts and low contrast degraded due to wavelength-dependent light scatter and abortion of the underwater environment, which impacts the application of high-level computer vision tasks. Considering the characteristics of uneven degradation and loss of color channel of underwater images, a novel dual attention transformer-based underwater image enhancement method, called UDAformer, is proposed. Specifically, Dual Attention Transformer Block (DATB) combining Channel Self-Attention Transformer (CSAT) with Pixel Self-Attention Transformer is proposed for efficient encoding and decoding of underwater image features. Then, the shifted window method for the pixel self-attention (SW-PSAT) is proposed to improve computational efficiency. Finally, the underwater images are recovered through the design of residual connections based on the underwater imaging model. Experimental results demonstrate the proposed UDAformer surpasses previous state-of-the-art methods, both qualitatively and quantitatively. The code is publicly available at: https://github.com/ShenZhen0502/UDAformer.