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Optical Engineering

Noise-resistant structure-preserving multiscale image decomposition
Author(s): Xin Jin; Xiaotong Wang; Xiaogang Xu; Chengyong Shao; Guanlei Xu
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Paper Abstract

A challenge for current edge-preserving image decompositions is to deal with noisy images. Gradient- or magnitude difference-based techniques regard the noise boundary as edges, while the local extrema-based method suffers from averaging noisy envelops. We introduce local anisotropy derived from nonlinear local structure tensor to differentiate edges from fine-scale details and noises. Providing low smoothness weights to the places with large local anisotropy rather than a large gradient under the improved weighted least squares optimization framework, we present a noise-resistant, structure-preserving smoothing operator. By either progressively or recursively applying this operator we construct our structure-preserving multiscale image decomposition. Based on the key property of our algorithm, noise resistance, we compare our results with existing edge-preserving image decomposition methods and demonstrate the effectiveness and robustness of our structure-preserving decompositions in the context of image restoration, noisy image abstraction, and noisy image dehazing.

Paper Details

Date Published: 9 August 2012
PDF: 12 pages
Opt. Eng. 51(8) 087002 doi: 10.1117/1.OE.51.8.087002
Published in: Optical Engineering Volume 51, Issue 8
Show Author Affiliations
Xin Jin, Dalian Naval Academy (China)
Xiaotong Wang, Dalian Naval Academy (China)
Xiaogang Xu, Dalian Naval Academy (China)
Chengyong Shao, Dalian Naval Academy (China)
Guanlei Xu, Dalian Naval Academy (China)


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