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Joint Priors-Based Restoration Method for Degraded Images Under Medium Propagation

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14435))

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Abstract

The deep learning-based methods have shown promising performance in restoring degraded images such as underwater and haze images. However, the majority of existing methods rely on simplified imaging models, which limits their generalization and applicability in real-world scenarios. To address these issues, we incorporate the imaging mechanism in complex underwater environments to redefine the imaging model for degraded images under medium propagation. We then propose a multi-stage restoration framework that combines model-based iterative optimization methods and deep learning methods. At the same time, to tackle the problem of inaccurate parameter estimation in methods relying on a single prior, we introduce a regularization design based on joint priors and develop an attention-based color correction network to correct color distortions in the degraded images. Experimental results on real-world degraded images demonstrate the effectiveness and superiority of our method in both quantitative and subjective evaluations when compared to state-of-the-art methods.

Supported by the Science and Technology Project of Guangzhou under Grant 202103010003, Science and Technology Project in key areas of Foshan under Grant 2020001006285.

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Author information

Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Hongsheng Chen, Wenbin Zou, Hongxia Gao, Weipeng Yang & Shasha Huang

  2. KUKA Robotics Guangdong Co., Ltd., Foshan, China

    Jianliang Ma

Authors
  1. Hongsheng Chen
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  2. Wenbin Zou
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  3. Hongxia Gao
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  4. Weipeng Yang
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  5. Shasha Huang
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  6. Jianliang Ma
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Corresponding author

Correspondence to Hongxia Gao .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, H., Zou, W., Gao, H., Yang, W., Huang, S., Ma, J. (2024). Joint Priors-Based Restoration Method for Degraded Images Under Medium Propagation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14435. Springer, Singapore. https://doi.org/10.1007/978-981-99-8552-4_27

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  • DOI: https://doi.org/10.1007/978-981-99-8552-4_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8551-7

  • Online ISBN: 978-981-99-8552-4

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