Dual-Domain Learning for JPEG Artifacts Removal

JPEG compression brings artifacts into the compressed image, which not only degrades visual quality but also affects the performance of other image processing tasks. Many learning-based compression artifacts removal methods have been developed to address this issue in recent years, with remarkable success. However, existing learning-based methods generally only exploit spatial information and lack exploration of frequency domain information. Exploring frequency domain information is critical because JPEG compression is actually performed in the frequency domain using the Discrete Cosine Transform (DCT). To effectively leverage information from both the spatial and frequency domains, we propose a novel Dual-Domain Learning Network for JPEG artifacts removal (D2LNet). Our approach first transforms the spatial domain image to the frequency domain by the fast Fourier transform (FFT). We then introduce two core modules, Amplitude Correction Module (ACM) and Phase Correction Module (PCM), which facilitate interactive learning of spatial and frequency domain information. Extensive experimental results performed on color and grayscale images have clearly demonstrated that our method achieves better results than the previous state-of-the-art methods. Code will be available at https://github.com/YeunkSuzy/Dual_Domain_Learning .