FreqMamba: Generalizable Frequency-Spatial Mamba Network for Structure-Preserving Retinal Image Enhancement

Retinal fundus image enhancement is a crucial prerequisite for reliable ophthalmic diagnosis and downstream clinical analyses. However, state-of-the-art automated segmentation models suffer severe performance degradation when applied to clinical images due to the domain gap caused by heterogeneous, frequency-dependent artifacts. Existing enhancement networks often struggle with cross-dataset generalization, tending to over-smooth anatomical details or introduce hallucinatory artifacts. To address this, FreqMamba, a novel Frequency-Spatial Hybrid State Space Model, is proposed for generalizable and structure-preserving retinal image enhancement. The core contribution is the Frequency-Spatial Mamba Block (FSMB), which elegantly decouples degradation restoration into dual domains. The spatial branch utilizes bidirectional Vision Mamba to capture global vascular continuity with linear complexity. Concurrently, the frequency branch introduces a learnable channel-wise modulation mechanism guided by a physical Butterworth-like high-frequency prior. Optimized via task-aware spectral-spatial constraints, this mechanism implicitly maintains a mathematical residual formulation to inject high-frequency details without spectrum explosion. Comprehensive experiments demonstrate that FreqMamba exhibits exceptional zero-shot generalization across diverse clinical datasets (e.g., achieving state-of-the-art PSNR across all datasets and an SSIM of 0.854 on CHASE). Compared to recent state-of-the-art methods, it significantly boosts downstream segmentation robustness (e.g., raising the Dice score from 0.508 to 0.531 on DRIVE under severe degradations), establishing a reliable prerequisite for clinical deployment.