R-FSACP-Net: A Robust Frequency–Spatial and Adaptive Channel Prior Framework for Underwater Image Enhancement

Abstract

Underwater image enhancement is challenging due to complex and highly variable degradations, including color attenuation, low contrast, and uneven illumination. Many existing methods perform well under limited conditions but lack robustness when image characteristics change across scenes. This paper presents R-FSACP-Net, a robust frequency-spatial and adaptive channel prior framework for underwater image enhancement. The proposed method explicitly addresses robustness by jointly modeling frequency-domain and spatial-domain features to stabilize enhancement across diverse degradation patterns. An initial frequency-spatial processing module reduces noise and illumination imbalance while preserving structural information. To handle color distortion, an adaptive channel prior module learns scene-aware color representations through cross-attention with multi-scale image features, improving generalization without relying on fixed assumptions. The refined features are further integrated to produce the final enhanced image. Extensive experiments on benchmark datasets show that the proposed approach achieves a PSNR of 25.818, SSIM of 0.949, and UIQM of 0.993 on the SUIM-E dataset. The results demonstrate the robustness and effectiveness of the proposed framework in complex underwater environments in Figure 1.