Frequency Decomposition-Driven Network for JPEG Artifacts Removal

Ke Cao, Xuanhua He, Keyu Yan, Tao Hu, Rui Li, Chengjun Xie, Jie Zhang

Abstract: JPEG compression, a widely adopted image format, often introduces visual artifacts and quality degradation in image quality. Removal of these JPEG artifacts, especially under high compression rates, proves challenging and typically results in overly smoothed images. This issue primarily arises due to the prevalence of low-frequency regions in natural images. This distribution leads models towards capturing low-frequency information and generating over-smoothing results. To address this issue, we propose the Frequency Decomposition-Driven Network (FDDNet) for JPEG artifact removal. FDDNet incorporates three core modules: the Decomposition Module (DM), inspired by wavelet lifting schemes, extracts both low-frequency and high-frequency components by considering feature channel relationships. The lightweight Low-Frequency Restoration Module and the High-Frequency Refinement Module, are each adept at handling distinct frequency components effectively. By emphasizing high-frequency components, our method surpasses existing approaches in terms of both quantitative metrics and visual quality across various datasets.