DRWKV: Focusing on Object Edges for Low-Light Image Enhancement
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arXiv
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| Format: | Preprint |
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2025
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| author | Bai, Xuecheng Wang, Yuxiang Hu, Boyu Jie, Qinyuan Xu, Chuanzhi Li, Kechen Xiao, Hongru Chung, Vera |
| author_facet | Bai, Xuecheng Wang, Yuxiang Hu, Boyu Jie, Qinyuan Xu, Chuanzhi Li, Kechen Xiao, Hongru Chung, Vera |
| contents | Low-light image enhancement remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and edge structures for enhanced edge fidelity. Secondly, we introduce Evolving WKV Attention, a spiral-scanning mechanism that captures spatial edge continuity and models irregular structures more effectively. Thirdly, we design the Bilateral Spectrum Aligner (Bi-SAB) and a tailored MS2-Loss to jointly align luminance and chrominance features, improving visual naturalness and mitigating artifacts. Extensive experiments on five LLIE benchmarks demonstrate that DRWKV achieves leading performance in PSNR, SSIM, and NIQE while maintaining low computational complexity. Furthermore, DRWKV enhances downstream performance in low-light multi-object tracking tasks, validating its generalization capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18594 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DRWKV: Focusing on Object Edges for Low-Light Image Enhancement Bai, Xuecheng Wang, Yuxiang Hu, Boyu Jie, Qinyuan Xu, Chuanzhi Li, Kechen Xiao, Hongru Chung, Vera Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Low-light image enhancement remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and edge structures for enhanced edge fidelity. Secondly, we introduce Evolving WKV Attention, a spiral-scanning mechanism that captures spatial edge continuity and models irregular structures more effectively. Thirdly, we design the Bilateral Spectrum Aligner (Bi-SAB) and a tailored MS2-Loss to jointly align luminance and chrominance features, improving visual naturalness and mitigating artifacts. Extensive experiments on five LLIE benchmarks demonstrate that DRWKV achieves leading performance in PSNR, SSIM, and NIQE while maintaining low computational complexity. Furthermore, DRWKV enhances downstream performance in low-light multi-object tracking tasks, validating its generalization capabilities. |
| title | DRWKV: Focusing on Object Edges for Low-Light Image Enhancement |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.18594 |