Low-Light Image Restoration Method Based on Visual State-Space Model and Frequency-Domain Enhancement

This paper addresses the challenges in low-light image enhancement, including weak global modeling capabilities and the difficulty in coordinating computational efficiency with detail preservation. We propose a fusion framework based on the Visual State Space Model (VSSM) and frequency-domain dynamic enhancement. By establishing a cross-domain collaborative mechanism, we innovatively design a dark-channel prior-guided parallel feature extraction and an illumination-reflection decoupling reconstruction strategy to achieve a physically interpretable enhancement process. The core algorithm consists of an Efficient Visual Scanning module (EVS) and a Discriminative Frequency-Domain Feedforward Network (EDFFN). The EVS module uses dynamic geometric transformation and a noise-aware scanning mechanism to model cross-regional spatial correlations while maintaining linear complexity. The EDFFN module achieves a dynamic balance between high-frequency detail enhancement and noise suppression through time-frequency dual-domain processing paths and adaptive band-pass filtering techniques. These two modules form a closed-loop optimization system of "long-range spatial modeling - local frequency-domain enhancement" through luminance gating fusion, improving model transparency and breaking through the performance bottlenecks of traditional methods.Experimental results show that our method significantly outperforms mainstream algorithms in terms of image quality, dynamic range expansion, and texture detail preservation. It effectively balances noise suppression and naturalness preservation, and recovers details in dark areas and preserves colors in extreme low-light scenes. Its high computational efficiency enables real-time processing of high-resolution images, providing an innovative solution for real-time visual enhancement scenarios such as intelligent security and autonomous driving.