PGFormer: Prompt guide network for underwater image enhancement

Underwater images are often influenced by light scattering and refraction, which leads to color deviation and poor quality. The enhancement of underwater images is significant for high-level semantic learning but also challenging. In this paper, we introduce PGFormer, a novel underwater image enhancement network that leverages prompt priors by integrating global and local prior information to improve underwater image quality. PGFormer comprises a global local enhancement module (GLEM) and a prompt-guided forward feedback network (PGFN). The GLEM extracts robust feature information through global and local feature modulation, whereas PGFN introduces prompt information into the local optimization process to further enhance local expression and refinement. Extensive experiments on various underwater datasets show that our method outperforms existing state-of-the-art techniques in terms of both visual quality and quantitative performance.