Image Denoising Using Total Variation Model Guided by Steerable Filter | Zendy

Wenxue Zhang | Zendy; Yongzhen Cao | Zendy; Rongxin Zhang | Zendy; Yuanquan Wang | Zendy

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Image Denoising Using Total Variation Model Guided by Steerable Filter

Author(s) -

Wenxue Zhang,

Yongzhen Cao,

Rongxin Zhang,

Yuanquan Wang

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/423761

Subject(s) - filter (signal processing) , computer vision , noise reduction , image (mathematics) , artificial intelligence , enhanced data rates for gsm evolution , diffusion , process (computing) , noise (video) , computer science , bilateral filter , energy (signal processing) , image denoising , median filter , image processing , algorithm , mathematics , physics , statistics , thermodynamics , operating system

We propose an adaptive total variation (TV) model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression

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