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Learning graph-constrained cascade regressors for single image super-resolution

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Abstract

Learning cascade regression has been shown an effective strategy to further enhance the perceptual quality of resulted high-resolution (HR) images. However, previous cascade regression-based SR methods have two obvious weaknesses: (1)edge structures cannot be preserved well when applying texture features to represent low-resolution (LR) images, and (2)the local manifold structures spanned by the LR-HR feature spaces cannot be revealed by the learned local linear mappings. To alleviate the aforementioned problems, a novel example regression-based super-resolution (SR) approach called learning graph-constrained cascade regressors (LGCCR) is presented, which learns a group of multi-round residual regressors in a unique way. Specifically, we improve the edge preservation capability by synthesizing the whole HR image rather than local image patches, which facilitates to extract the edge features to represent LR images. Moreover, we utilize a graph-constrained regression model to build the local linear regressors, where each local linear regressor responds to an anchored atom in the learned over-complete dictionary. Both quantitative and qualitative quality evaluations on seven benchmark databases indicate the superiority of the proposed LGCCR-based SR approach in comparing with other state-of-the-art SR predecessors.

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Acknowledgment

The authors would like to thank the Associate Editor and the anonymous reviewers for their constructive and insightful comments on this paper. This work was supported in part by the National Natural Science Foundation of China under Grant 61971339, Grant 61471161, and Grant 61972136, in part by the Key Project of the Natural Science Foundation of Shaanxi Province under Grant 2018JZ6002 and Grant 2018GY-173, in part by the Textile Intelligent Equipment Information and Control Innovation Team of Shaanxi Innovation Ability Support Program under Grant 2021TD-29, in part by the Science and Technology Planning Project of Xi’an under Grant 2020KJRC0028, in part by the Technology Planning Project of Beilin, Xi’an under Grant GX2006, and in part by Natural Science Basic Research Program of Shaanxi under Grant 2021JM-452.

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jianqiang Yan

  2. School of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, China

    Kaibing Zhang, Shuang Luo, Jian Xu & Jian Lu

  3. School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China

    Zenggang Xiong

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  1. Jianqiang Yan
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  2. Kaibing Zhang
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  3. Shuang Luo
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  6. Zenggang Xiong
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Correspondence to Kaibing Zhang.

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Yan, J., Zhang, K., Luo, S. et al. Learning graph-constrained cascade regressors for single image super-resolution. Appl Intell 52, 10867–10884 (2022). https://doi.org/10.1007/s10489-021-02904-3

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