Jingxuan Zhang

In recent years, underwater image super-resolution (SR) reconstruction has increasingly become a core focus of underwater machine vision. Light scattering and refraction in underwater environments result in images with blurred details, low contrast, color distortions, and multiple visual artifacts. Despite the promising results achieved by deep learning in underwater SR tasks, global and frequency-domain information remain poorly addressed. In this study, we introduce a novel underwater SR method based on the Vision State-Space Model, dubbed MambaUSR. At its core, we design the Frequency State-Space Module (FSSM), which integrates two complementary components: the Visual State-Space Module (VSSM) and the Frequency-Assisted Enhancement Module (FAEM). The VSSM models long-range dependencies to enhance global structural consistency and contrast, while the FAEM employs Fast Fourier Transform combined with channel attention to extract high-frequency details, thereby improving the fidelity and naturalness of reconstructed images. Comprehensive evaluations on benchmark datasets confirm that MambaUSR delivers superior performance in underwater image reconstruction.