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Learning stacking regressors for single image super-resolution

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Abstract

Example learning-based single image super-resolution (SR) technique has been widely recognized for its effectiveness in restoring a high-resolution (HR) image with finer details from a given low-resolution (LR) input. However, most popular approaches only choose one type of image features to learn the mapping relationship between LR and HR images, making it difficult to fit into the diversity of different natural images. In this paper, we propose a novel stacking learning-based SR framework by extracting both the gradient features and the texture features of images simultaneously to train two complementary models. Since the gradient features are helpful to represent the edge structures while the texture features are beneficial to restore the texture details, the newly proposed method cleverly combines the merits of two complementary features and makes the resultant HR images more faithful to their original counterparts. Moreover, we enhance the SR capacity by using a residual cascaded scheme to further reduce the gap between the super-resolved images and the corresponding original images. Experimental results carried out on seven benchmark datasets indicate that the proposed SR framework performs better than other seven state-of-the-art SR methods in both quantitative and qualitative quality assessments.

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Acknowledgments

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, in part by the Doctoral Startup Foundation of Xi’an Polytechnic University under Grant BS1616, in part by Graduate Scientific Innovation Fund for Xi’an Polytechnic University under Grant chx2020015, and in part by the Youth Innovation Team of Shaanxi Universities.

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Authors and Affiliations

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

    Kaibing Zhang, Shuang Luo, Minqi Li, Junfeng Jing & Jian Lu

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

    Zenggang Xiong

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  1. Kaibing Zhang
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  2. Shuang Luo
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  3. Minqi Li
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  4. Junfeng Jing
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  5. Jian Lu
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  6. Zenggang Xiong
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Correspondence to Kaibing Zhang.

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Zhang, K., Luo, S., Li, M. et al. Learning stacking regressors for single image super-resolution. Appl Intell 50, 4325–4341 (2020). https://doi.org/10.1007/s10489-020-01787-0

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