Degradation-Robust Hue Prior Network for Low-Light Rainy Image Restoration

Abstract

Restoring images captured in low-light rainy scenes is challenging because brightness degradation and rain corruption are strongly coupled. Enhancing visibility may amplify hidden rain streaks and noise, whereas aggressive deraining can suppress already weak scene structures. Existing cascaded pipelines and general restoration models often struggle to handle this interaction effectively. In this paper, we present the Degradation-Robust Hue Prior Network (DHP-Net), a single-stage framework for low-light rainy image restoration that combines degradation-robust hue prior guidance with perturbation-aware feature modulation. Specifically, DHP-Net extracts multi-scale hue priors to provide stable structural and color cues under coupled degradations, and it injects them into a hierarchical Transformer restoration backbone. To further improve interaction among entangled feature responses, we introduce a Channel-adaptive Attention Perturbation Module that reorganizes intermediate representations before cross-channel aggregation. In this way, the proposed model jointly promotes visibility enhancement, rain removal, and structure preservation within a unified architecture. Extensive experiments on the Low-Light Rain (LLR) benchmark show that DHP-Net achieves 33.14 dB Peak Signal-to-Noise Ratio (PSNR) and 0.9252 Structural Similarity Index Measure (SSIM) on synthetic data and also delivers superior perceptual quality on real-world low-light rainy images, consistently outperforming existing state-of-the-art restoration models.