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arXiv · 2504.02373

HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement

Hantang Li·Qiang Zhu·Xiandong Meng·Lei Xiong·Shuyuan Zhu·Xiaopeng Fan
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Abstract

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.

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Hantang Li, Qiang Zhu, Xiandong Meng, Lei Xiong, Shuyuan Zhu, Xiaopeng Fan. 2025-04-03. HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement. https://arxiv.org/abs/2504.02373

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