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Super-resolution image reconstruction using fractional-order total variation and adaptive regularization parameters

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Abstract

Single-image super-resolution (SR) reconstruction aims to obtain a high-resolution (HR) image from a low-resolution (LR) image. In this paper, a hybrid single-image SR model integrated total variation (TV) and fractional-order TV (FOTV) is proposed to pursuit the adaptive reconstruction of the HR image. Specifically, fractional order in the proposed SR model is adaptively set according to the textural feature of the LR image firstly; then, the SR model is separated into two sub-models with each of them containing exactly one regularization parameter. These two sub-models are solved by using alternating direction multiplier method, and two regularization parameters are concurrently updated by using discrepancy principle. Finally, the solutions of two sub-models are interactively averaged to reconstruct HR image. The results of experiments indicated that the proposed hybrid SR model with adaptive regularization parameters has a comparative performance compared with state-of-the-art methods. Moreover, it would be potentially more adaptive for the condition of varied blurred kernels.

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Authors and Affiliations

  1. College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, China

    Xiaomei Yang, Jiawei Zhang, Yanan Liu, Xiujuan Zheng & Kai Liu

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  1. Xiaomei Yang
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  2. Jiawei Zhang
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Correspondence to Xiujuan Zheng.

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Yang, X., Zhang, J., Liu, Y. et al. Super-resolution image reconstruction using fractional-order total variation and adaptive regularization parameters. Vis Comput 35, 1755–1768 (2019). https://doi.org/10.1007/s00371-018-1570-2

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