中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Adaptive total variation denoising based on difference curvature
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Adaptive total variation denoising based on difference curvature
Qiang Chen; Philippe Montesinos; Quan Sen Sun; Peng Ann Heng; De Shen Xia
2010
Source PublicationIMAGE AND VISION COMPUTING
Volume28Issue:3Pages:298–306
Subtype期刊论文
AbstractImage denoising methods based on gradient dependent regularizers such as Rudin et al.'s total variation (TV) model often suffer the staircase effect and the loss of fine details. In order to overcome such drawbacks, this paper presents an adaptive total variation method based on a new edge indicator, named difference curvature, which can effectively distinguish between edges and ramps. With adaptive regularization and fidelity terms, the new model has the following properties: at object edges, the regularization term is approximate to the TV norm in order to preserve the edges, and the weight of the fidelity term is large in order to preserve details; in flat and ramp regions, the regularization term is approximate to the L2 norm in order to avoid the staircase effect, and the weight of the fidelity term is small in order to strongly remove the noise. Comparative results on both synthetic and natural images demonstrate that the new method can avoid the staircase effect and better preserve fine details.
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Indexed BySCI
Language英语
Department人机交互研究中心
Document Type期刊论文
Identifierhttp://ir.siat.ac.cn/handle/172644/2720
Collection集成所
AffiliationIMAGE AND VISION COMPUTING
Recommended Citation
GB/T 7714
Qiang Chen,Philippe Montesinos,Quan Sen Sun,et al. Adaptive total variation denoising based on difference curvature[J]. IMAGE AND VISION COMPUTING,2010,28(3):298–306.
APA Qiang Chen,Philippe Montesinos,Quan Sen Sun,Peng Ann Heng,&De Shen Xia.(2010).Adaptive total variation denoising based on difference curvature.IMAGE AND VISION COMPUTING,28(3),298–306.
MLA Qiang Chen,et al."Adaptive total variation denoising based on difference curvature".IMAGE AND VISION COMPUTING 28.3(2010):298–306.
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