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Research Article | Open Access

A nonlocal gradient concentration method for image smoothing

Qian Liu1, Caiming Zhang1,2, Qiang Guo1,2, Yuanfeng Zhou1( )
School of Computer Science and Technology, Shandong University, Jinan 250101, China.
Shandong University of Finance and Economics, Shandong Provincial Key Laboratory of Digital Media Technology, Jinan 250014, China.
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Abstract

It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradient-sparsity constraint, allowing details throughout the whole image to be removed automatically in a data-driven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal, and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications, including edge manipulation, image abstraction, detail magnification, and image resizing, show the applicability of the new method.

Keywords

image smoothingnonlocal similarityL0 normedge detection

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Computational Visual Media
Volume 1 Issue 3,
September 2015
Pages 197-209
DOI: 10.1007/s41095-015-0012-6
Cite this article:
Liu Q, Zhang C, Guo Q, et al. A nonlocal gradient concentration method for image smoothing. Computational Visual Media, 2015, 1(3): 197-209. https://doi.org/10.1007/s41095-015-0012-6

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Revised: 19 December 2014
Accepted: 02 April 2015
Published: 14 August 2015
© The Author(s) 2015

This article is published with open access at Springerlink.com

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Other papers from this open access journal are available at no cost from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
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