Rethinking underwater image enhancement via a hybrid Mamba–transformer network

Abstract Underwater imaging is simultaneously affected by non-uniform illumination and multi-path light scattering, often resulting in severe degradations such as color distortion, contrast reduction, and detail blurring. These degradations substantially hinder the performance of high-level vision tasks, including underwater object detection. To address these challenges, this paper proposes a hybrid Mamba–Transformer network for underwater image enhancement, centered on a Hybrid Mamba–Transformer Block that couples local state-space modeling with phase-guided frequency-domain self-attention. Specifically, the local state-space branch employs four-directional selective scanning to capture local structural information and texture continuity, while the frequency-domain self-attention branch exploits phase information to model complementary frequency-aware contextual correlations. Through their collaborative modeling, the proposed framework provides a unified representation of local structural degradation and frequency-related feature corruption in underwater images. Complementing this core design, a hybrid depth convolutional feed-forward network and a wavelet-based downsampling block are incorporated to further refine cross-channel features, suppress noise interference, and preserve structural details during feature transformation. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method consistently achieves competitive or superior enhancement performance with relatively low model complexity, validating its effectiveness and robustness for underwater image enhancement.