Underwater images often suffer from strong color casts, low contrast, and blurred textures. It is observed that low resolution can provide globally correct color, so low-resolution priors can guide high-resolution correction. While many recent methods combine Transformer and CNN components, Mamba offers an efficient alternative for global dependency modeling. Motivated by these insights, this paper proposes a cross- and inter-resolution state-space model for underwater image enhancement (CIR-SSM). The method consists of three sub-networks at full, 1/2, and 1/4 resolutions, each stacking color–texture Mamba modules. Each module includes a color Mamba block, a texture Mamba block, and a color–texture fusion Mamba block. The color Mamba block injects low-resolution color priors into the state-space trajectory to steer global color reconstruction in the high-resolution branch. In parallel, the texture Mamba block operates at the native resolution to capture fine-grained structural dependencies for texture restoration. The fusion Mamba block adaptively merges the enhanced color and texture representations within the state-space framework to produce the restored image. Comprehensive quantitative assessments on both the UIEB and SQUID benchmarks show that the proposed framework achieves the highest evaluated scores, outperforming several representative state-of-the-art methods.
Liu et al. (Tue,) studied this question.