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.
Abstract (translated)
在实际应用中，传统方法生成了大量的低光图像，这些图像需要压缩以实现高效存储和传输。然而，大多数现有方法要么忽略了在增强过程中去除潜在的压缩伪影，要么未能建立一个统一的框架来处理不同压缩质量下的图像任务联合增强问题。为了解决这个问题，我们提出了混合先验引导网络（HPGN），该网络通过结合压缩和光照先验来增强被压缩的低光图像。我们的方法充分利用JPEG质量因子（QF）和DCT量化矩阵（QM）来指导高效的任务联合插件模块的设计。此外，我们采用随机生成QF策略来引导模型训练，使单一模型能够处理不同压缩级别的图像增强任务。实验结果证实了我们所提出的方法的优越性。
URL
https://arxiv.org/abs/2504.02373
