Noise-resistant structure-preserving multiscale image decomposition

Xin Jin; Xiaotong Wang; Xiaogang XuGuanlei XuChengyong Shao

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Noise-resistant structure-preserving multiscale image decomposition

机译：Noise-resistant structure-preserving multiscale image decomposition

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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.

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来源
《Optical Engineering》 |2012年第8期|087002-1-087002-11|共11页
作者
Xin Jin; Xiaotong Wang; Xiaogang XuGuanlei XuChengyong Shao;
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作者单位

Dalian Naval Academy, Department of Navigation, Dalian 116018, China;

Dalian Naval Academy, Department of Arming System and Automation, Dalian 116018, China;

Dalian Naval Academy, Department of Military Oceanography, Dalian 116018, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
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Noise-resistant structure-preserving multiscale image decomposition

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