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Dual-Domain Learning for JPEG Artifacts Removal

EasyChair Preprint no. 11261

13 pagesDate: November 7, 2023


JPEG compression brings artifacts into the compressed image, which not only degrade visual quality, but also affect the performance of other image processing tasks. To address this issue, many learning-based compression artifacts removal methods have been developed 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

Keyphrases: Dual-Domain Learning, Fourier transform, JPEG Artifacts Removal

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Guang Yang and Lu Lin and Chen Wu and Feng Wang},
  title = {Dual-Domain Learning for JPEG Artifacts Removal},
  howpublished = {EasyChair Preprint no. 11261},

  year = {EasyChair, 2023}}
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