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A Total Fractional-Order Variation Model for Image Super-Resolution and Its SAV Algorithm

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Abstract

Single-image super-resolution reconstruction aims to obtain a high-resolution image from a low-resolution image. Since the super-resolution problem is ill-posed, it is common to use a regularization technique. However, the choice of the fidelity and regularization terms is not obvious, and it plays a major role in the quality of the desired high resolution image. In this paper, a hybrid single-image super-resolution model integrated with total variation (TV) and fractional-order TV is proposed to provide an effective reconstruction of the HR image. We develop an efficient numerical scheme for this model using the scalar auxiliary variable approach with an adaptive time stepping strategy. Thorough experimental results suggest that the proposed model and numerical scheme can reconstruct high quality results both quantitatively and perceptually.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (11971131, 11971407, U1637208, 61873071, 51476047, 11871133), NSF DMS-1720442, the Natural Science Foundation of Heilongjiang Province (LC2018001, A2016003).

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Authors and Affiliations

  1. School of Mathematics, Harbin Institute of Technology, Harbin, China

    Wenjuan Yao, Zhichang Guo, Jiebao Sun & Boying Wu

  2. Department of Mathematics, Purdue University, West Lafayette, IN, 47907, USA

    Jie Shen

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  1. Wenjuan Yao
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  2. Jie Shen
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  3. Zhichang Guo
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Correspondence to Zhichang Guo.

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Yao, W., Shen, J., Guo, Z. et al. A Total Fractional-Order Variation Model for Image Super-Resolution and Its SAV Algorithm. J Sci Comput 82, 81 (2020). https://doi.org/10.1007/s10915-020-01185-1

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