HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement
Journal:
arXiv
Published Date:
Apr 3, 2025
Abstract
In practical applications, conventional methods generate large volumes of
low-light images that require compression for efficient storage and
transmission. However, most existing methods either disregard the removal of
potential compression artifacts during the enhancement process or fail to
establish a unified framework for joint task enhancement of images with varying
compression qualities. To solve this problem, we propose the hybrid
priors-guided network (HPGN), which 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 joint task plug-and-play modules. Additionally, we
employ a random QF generation strategy to guide model training, enabling a
single model to enhance images across different compression levels.
Experimental results confirm the superiority of our proposed method.
