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Research Article | Open Access

Multi-example feature-constrained back-projection method for image super-resolution

Junlei Zhang1, Dianguang Gai2, Xin Zhang1, Xuemei Li1( )
School of Computer Science and Technology, Shandong University, Jinan, 250101, China.
Earthquake Administration of Shandong Province, China.
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Abstract

Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive kNN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image. Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Keywords

feature constraintsback-projectionsuper-resolution (SR)

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Computational Visual Media
Volume 3 Issue 1,
March 2017
Pages 73-82
DOI: 10.1007/s41095-016-0070-4
Cite this article:
Zhang J, Gai D, Zhang X, et al. Multi-example feature-constrained back-projection method for image super-resolution. Computational Visual Media, 2017, 3(1): 73-82. https://doi.org/10.1007/s41095-016-0070-4

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Revised: 09 September 2016
Accepted: 22 December 2016
Published: 17 March 2017
© The Author(s) 2016

This article is published with open access at Springerlink.com

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