HRMamba: A Hybrid Retinex and State-Space Model for Underwater Image Enhancement

Underwater light absorption and scattering lead to severe color distortion, reduced visibility, contrast loss, and a significant degradation in image quality, thereby impeding both human visual analysis and machine vision tasks. Although considerable progress has been achieved in improving image quality, existing deep learning-based methods for underwater image enhancement (UIE) remain constrained by high computational complexity and insufficient modeling of global dependencies, which restricts their practical deployment in resource-limited underwater environments. To tackle these issues, we propose a novel hybrid framework integrating Retinex theory and state-space models (SSMs) for underwater image enhancement, named HRMamba. Different from existing Transformer-based approaches constrained by quadratic complexity, HRMamba attains computational efficiency through linear-complexity state-space operations while maintaining global dependency modeling capabilities. Moreover, to achieve comprehensive feature fusion, an Illumination Feature Fusion Module (IFFM) is proposed, which synergizes the global dependency modeling of SSMs with the local adaption capability of convolutional neural networks (CNNs). For context-sensitive noise suppression with illumination awareness, we propose an Illumination-Guided Denoising Module (IGDM) that employs directional-scanning Vision State Space Module (VSSM) blocks. Experiments demonstrate that HRMamba achieves state-of-the-art enhancement quality via an efficient architecture, significantly improving color fidelity and visibility restoration while substantially reducing computational demands. The code is available at https://github.com/YeFan-web/HRMamba/.