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Degradation-Robust Hue Prior Network for Low-Light Rainy Image Restoration
Pujing Hu1, Yixiao Liu2, Xiaodong Luo3
1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
The Degradation-Robust Hue Prior Network (DHP-Net) effectively restores low-light rainy images by integrating hue priors and attention mechanisms. This novel approach enhances visibility while preserving scene structures, outperforming existing methods.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Low-light rainy image restoration is complex due to coupled brightness degradation and rain.
- Existing methods struggle to balance visibility enhancement with structure preservation and rain removal.
Purpose of the Study:
- To develop a unified framework for simultaneous low-light enhancement and deraining.
- To address the challenge of coupled degradations in rainy image restoration.
Main Methods:
- Introduced the Degradation-Robust Hue Prior Network (DHP-Net), a single-stage restoration framework.
- Utilized multi-scale hue priors for stable structural and color cues under degradation.
- Incorporated a Channel-adaptive Attention Perturbation Module for improved feature interaction.
Main Results:
- DHP-Net achieved 33.14 dB PSNR and 0.9252 SSIM on synthetic data.
- Demonstrated superior perceptual quality on real-world low-light rainy images.
- Consistently outperformed state-of-the-art restoration models.
Conclusions:
- DHP-Net offers an effective solution for joint visibility enhancement, rain removal, and structure preservation.
- The proposed hue prior guidance and attention mechanism are crucial for handling coupled degradations.
- The framework shows significant potential for real-world applications in adverse weather conditions.
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