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Unfolded Deep Kernel Estimation for Blind Image Super-Resolution

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13678))

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Abstract

Blind image super-resolution (BISR) aims to reconstruct a high-resolution image from its low-resolution counterpart degraded by unknown blur kernel and noise. Many deep neural network based methods have been proposed to tackle this challenging problem without considering the image degradation model. However, they largely rely on the training sets and often fail to handle images with unseen blur kernels during inference. Deep unfolding methods have also been proposed to perform BISR by utilizing the degradation model. Nonetheless, the existing deep unfolding methods cannot explicitly solve the data term of the unfolding objective function, limiting their capability in blur kernel estimation. In this work, we propose a novel unfolded deep kernel estimation (UDKE) method, which, for the first time to our best knowledge, explicitly solves the data term with high efficiency. The UDKE based BISR method can jointly learn image and kernel priors in an end-to-end manner, and it can effectively exploit the information in both training data and image degradation model. Experiments on benchmark datasets and real-world data demonstrate that the proposed UDKE method could well predict complex unseen non-Gaussian blur kernels in inference, achieving significantly better BISR performance than state-of-the-art. The source code of UDKE is available at https://github.com/natezhenghy/UDKE.

This work is supported by the Hong Kong RGC RIF grant (R5001-18) and the PolyU-OPPO Joint Innovation Lab.

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Authors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

    Hongyi Zheng, Hongwei Yong & Lei Zhang

  2. OPPO Research, Shenzhen, People’s Republic of China

    Lei Zhang

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  1. Hongyi Zheng
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  2. Hongwei Yong
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  3. Lei Zhang
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Correspondence to Lei Zhang .

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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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Zheng, H., Yong, H., Zhang, L. (2022). Unfolded Deep Kernel Estimation for Blind Image Super-Resolution. 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 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-19797-0_29

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