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Dual-Domain Learning for JPEG Artifacts Removal

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1967))

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Abstract

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.

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Acknowledgements

This work is supported by National MCF Energy R &D Program of China (Grant No: 2018YFE0302100).

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Guang Yang & Chen Wu

  2. Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, China

    Feng Wang

  3. Central China Normal University, Wuhan, China

    Lu Lin

Authors
  1. Guang Yang
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  2. Lu Lin
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  3. Chen Wu
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  4. Feng Wang
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Contributions

Guang Yang and Lu Lin are contributed equally to this work.

Corresponding author

Correspondence to Chen Wu .

Editor information

Editors and Affiliations

  1. Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangdong, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Yang, G., Lin, L., Wu, C., Wang, F. (2024). Dual-Domain Learning for JPEG Artifacts Removal. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_42

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  • DOI: https://doi.org/10.1007/978-981-99-8178-6_42

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  • Print ISBN: 978-981-99-8177-9

  • Online ISBN: 978-981-99-8178-6

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