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Metric Learning Based Interactive Modulation for Real-World Super-Resolution

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13677))

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Abstract

Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from a severe performance drop when the real degradation is different from their assumptions. Such a limitation is due to the complexity of real-world degradations, which can not provide explicit supervision to the interactive modulation during training. However, how to realize the interactive modulation in real-world super-resolution has not yet been studied. In this work, we present a Metric Learning based Interactive Modulation for Real-World Super-Resolution (MM-RealSR). Specifically, we propose an unsupervised degradation estimation strategy to estimate the degradation level in real-world scenarios. Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner. Moreover, we introduce an anchor point strategy in the metric learning process to normalize the distribution of metric space. Extensive experiments demonstrate that the proposed MM-RealSR achieves excellent modulation and restoration performance in real-world super-resolution. Codes are available at https://github.com/TencentARC/MM-RealSR.

X. Wang—Project lead.

Chong Mou is an intern in ARC Lab, Tencent PCG.

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Acknowledgement

This work was partially supported by the Shenzhen Fundamental Research Program (No. GXWD20201231165807007-20200807164903001), National Natural Science Foundation of China (61906184, U1913210), and the Shanghai Committee of Science and Technology, China (Grant No. 21DZ1100100).

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Authors and Affiliations

  1. Peking University Shenzhen Graduate School, Shenzhen, China

    Chong Mou & Jian Zhang

  2. Peng Cheng Laboratory, Shenzhen, China

    Jian Zhang

  3. ARC Lab, Tencent PCG, Shenzhen, China

    Chong Mou, Yanze Wu, Xintao Wang & Ying Shan

  4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China

    Chao Dong

  5. Shanghai AI Laboratory, Shanghai, China

    Chao Dong

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  1. Chong Mou
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  2. Yanze Wu
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  4. Chao Dong
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  5. Jian Zhang
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  6. Ying Shan
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Corresponding author

Correspondence to Jian 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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Mou, C., Wu, Y., Wang, X., Dong, C., Zhang, J., Shan, Y. (2022). Metric Learning Based Interactive Modulation for Real-World 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 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_43

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