Prior-guided attention network for underwater image enhancement

Underwater images often suffer from color deviation, low contrast, blurring, and other degradation issues due to the attenuation characteristics of water and the presence of particles in the aquatic environment. In this paper, we propose an underwater image enhancement method based on the transformer architecture and underwater prior knowledge to achieve visually improved results. Specifically, we introduce a prior-guided attention network (PGANet), which comprises a prior-guided block (PGB) and a multi-feature attention block (MFAB). On the one hand, considering the varying degrees of color degradation, we employ the PGB to direct the network in capturing features that are significantly degraded. On the other hand, the multi-feature attention block is incorporated to explore rich-feature information at multiple scales in the underwater image. Experimental results demonstrate that our method effectively corrects color biases and removes haze across diverse underwater datasets.