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Scale-Aware Distillation Network for Lightweight Image Super-Resolution

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13021))

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Abstract

Many lightweight models have achieved great progress in single image super-resolution. However, their parameters are still too many to be applied in practical applications, and it still has space for parameter reduction. Meanwhile, multi-scale features are usually underutilized by researchers, which are better for multi-scale regions’ reconstruction. With the renaissance of deep learning, convolution neural network based methods has prompted many computer vision tasks (e.g., video object segmentation [21, 38, 40], human parsing [39], human-object interaction detection [39]) to achieve significant progresses. To solve this limitation, in this paper, we propose a lightweight super-resolution network named scale-aware distillation network (SDNet). SDNet is built on many stacked scale-aware distillation blocks (SDB), which contain a scale-aware distillation unit (SDU) and a context enhancement (CE) layer. Specifically, SDU enriches the hierarchical features at a granular level via grouped convolution. Meanwhile, the CE layer further enhances the multi-scale feature representation from SDU by context learning to extract more discriminative information. Extensive experiments are performed on commonly-used super-resolution datasets, and our method achieves promising results against other state-of-the-art methods with fewer parameters.

This is a student paper.

This work is supported by the National Natural Science Foundation of China (No. 61273273), by the National Key Research and Development Plan (No. 2017YFC0112001), and by China Central Television (JG2018-0247).

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Author information

Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Haowei Lu, Yao Lu, Gongping Li, Yanbei Sun, Shunzhou Wang & Yugang Li

Authors
  1. Haowei Lu
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  2. Yao Lu
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  3. Gongping Li
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  4. Yanbei Sun
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  5. Shunzhou Wang
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  6. Yugang Li
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Corresponding author

Correspondence to Yao Lu .

Editor information

Editors and Affiliations

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

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Lu, H., Lu, Y., Li, G., Sun, Y., Wang, S., Li, Y. (2021). Scale-Aware Distillation Network for Lightweight Image Super-Resolution. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-88010-1_11

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  • Online ISBN: 978-3-030-88010-1

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