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Learning Deep Non-blind Image Deconvolution Without Ground Truths

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13666))

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Abstract

Non-blind image deconvolution (NBID) is about restoring a latent sharp image from a blurred one, given an associated blur kernel. Most existing deep neural networks for NBID are trained over many ground truth (GT) images, which limits their applicability in practical applications such as microscopic imaging and medical imaging. This paper proposes an unsupervised deep learning approach for NBID which avoids accessing GT images. The challenge raised from the absence of GT images is tackled by a self-supervised reconstruction loss that approximates its supervised counterpart well. The possible errors of blur kernels are addressed by a self-supervised prediction loss based on intermediate samples as well as an ensemble inference scheme based on kernel perturbation. The experiments show that the proposed approach provides very competitive performance to existing supervised learning-based methods, no matter under accurate kernels or erroneous kernels.

Y. Quan—Is also with Pazhou Lab, Guangzhou 510335, China. He would like to thank the support in part by National Natural Science Foundation of China under Grant 61872151 and in part by Natural Science Foundation of Guangdong Province under Grant 2022A1515011755.

H. Ji—Would like thank the support in part by Singapore MOE AcRF under Grant R-146-000-315-114.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Yuhui Quan & Zhuojie Chen

  2. Department of Mathematics, National University of Singapore, Singapore, 119076, Singapore

    Yuhui Quan, Huan Zheng & Hui Ji

Authors
  1. Yuhui Quan
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  2. Zhuojie Chen
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  3. Huan Zheng
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  4. Hui Ji
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Corresponding author

Correspondence to Zhuojie Chen .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Quan, Y., Chen, Z., Zheng, H., Ji, H. (2022). Learning Deep Non-blind Image Deconvolution Without Ground Truths. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-20068-7_37

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