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

• URL: http://arxiv.org/abs/2504.02373v2
• Date: Thu, 18 Sep 2025 07:34:23 GMT
• Title: HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement
• Authors: Hantang Li, Qiang Zhu, Xiandong Meng, Lei Xiong, Shuyuan Zhu, Xiaopeng Fan,
• Abstract summary: We propose a hybrid priors-guided network (HPGN) that enhances compressed low-light images by integrating both compression and illumination priors.<n>Our approach fully utilizes the JPEG quality factor (QF) and DCT quantization matrix to guide the design of efficient plug-and-play modules for joint tasks.
• Score: 73.39195340852964
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 a 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 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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