HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918493154181120 |
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| author | Li, Hantang Zhu, Qiang Meng, Xiandong Xiong, Lei Zhu, Shuyuan Fan, Xiaopeng |
| author_facet | Li, Hantang Zhu, Qiang Meng, Xiandong Xiong, Lei Zhu, Shuyuan Fan, Xiaopeng |
| contents | In practical applications, low-light images are often compressed for efficient storage and transmission. Most existing methods disregard compression artifacts removal or hardly establish a unified framework for joint task enhancement of low-light images with varying compression qualities. To address this problem, we propose an efficient hybrid priors-guided network (HPGN) that enhances compressed low-light images by integrating both compression and illumination priors. Our approach fully utilizes the JPEG quality factor (QF) and DCT quantization matrix (QM) to guide the design of efficient plug-and-play modules for joint tasks. Additionally, we employ a random QF generation strategy to guide model training, enabling a single model to enhance low-light images with different compression levels. Experimental results demonstrate the superiority of our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement Li, Hantang Zhu, Qiang Meng, Xiandong Xiong, Lei Zhu, Shuyuan Fan, Xiaopeng Image and Video Processing Computer Vision and Pattern Recognition In practical applications, low-light images are often compressed for efficient storage and transmission. Most existing methods disregard compression artifacts removal or hardly establish a unified framework for joint task enhancement of low-light images with varying compression qualities. To address this problem, we propose an efficient hybrid priors-guided network (HPGN) that enhances compressed low-light images by integrating both compression and illumination priors. Our approach fully utilizes the JPEG quality factor (QF) and DCT quantization matrix (QM) to guide the design of efficient plug-and-play modules for joint tasks. Additionally, we employ a random QF generation strategy to guide model training, enabling a single model to enhance low-light images with different compression levels. Experimental results demonstrate the superiority of our proposed method. |
| title | HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.02373 |