Combined complex ridgelet shrinkage and total variation minimization

被引:21
|
作者
Ma, Jianwei
Fenn, Markus
机构
[1] Univ Oxford, Inst Math, Oxford Ctr Ind & Appl Math, Oxford OX1 3LB, England
[2] Univ Mannheim, Dept Math & Comp Sci, D-68131 Mannheim, Germany
来源
SIAM JOURNAL ON SCIENTIFIC COMPUTING | 2006年 / 28卷 / 03期
关键词
nonequispaced fast Fourier transform; ridgelets; complex wavelets; shift invariance; total variation minimization; detection of line singularities; surface characterization;
D O I
10.1137/05062737X
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
A new algorithm for the characterization of engineering surface topographies with line singularities is proposed. It is based on thresholding complex ridgelet coefficients combined with total variation (TV) minimization. The discrete ridgelet transform is designed by first using a discrete Radon transform based on the nonequispaced fast Fourier transform (NFFT) and then applying a dual-tree complex wavelet transform (DT CWT). The NFFT-based approach of the Radon transform completely avoids linear interpolations of the Cartesian-to-polar grid and requires only O(n(2) log n) arithmetic operations for n by n arrays, while its inverse preserves the good reconstruction quality of the filtered backprojection. The DT CWT in the second step of the ridgelet transform provides approximate shift invariance on the projections of the Radon transform. After hard thresholding the ridgelet coefficients, they are restored using TV minimization to eliminate the pseudo-Gibbs artifacts near the discontinuities. Numerical experiments demonstrate the remarkable ability of the methodology to extract line scratches.
引用
收藏
页码:984 / 1000
页数:17
相关论文
共 50 条
  • [1] Combined Shearlet Shrinkage and Total Variation Minimization for Image Denoising
    Mousavi, Zohre
    Lakestani, Mehrdad
    Razzaghi, Mohsen
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE, 2018, 42 (A1): : 31 - 37
  • [2] Combined Shearlet Shrinkage and Total Variation Minimization for Image Denoising
    Zohre Mousavi
    Mehrdad Lakestani
    Mohsen Razzaghi
    Iranian Journal of Science and Technology, Transactions A: Science, 2018, 42 : 31 - 37
  • [3] Reconstruction of ridgelet coefficients using total variation minimization
    Deng, Chengzhi
    Cao, Hanqiang
    Wang, Shengqian
    ICIEA 2007: 2ND IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-4, PROCEEDINGS, 2007, : 2411 - +
  • [4] Tetrolet shrinkage with anisotropic total variation minimization for image approximation
    Krommweh, Jens
    Ma, Jianwei
    SIGNAL PROCESSING, 2010, 90 (08) : 2529 - 2539
  • [5] Alternating Minimization Method for Total Variation Based Wavelet Shrinkage Model
    Zeng, Tieyong
    Li, Xiaolong
    Ng, Michael
    COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2010, 8 (05) : 976 - 994
  • [6] Shoreline detection algorithm based on total variation and ridgelet transform
    Zhang, Suoping
    Zhang, Chuntian
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2009, 30 (12): : 2580 - 2585
  • [7] Global total variation minimization
    Dibos, F
    Koepfler, G
    SIAM JOURNAL ON NUMERICAL ANALYSIS, 2000, 37 (02) : 646 - 664
  • [8] Total variation energy functional with restrictions on the finite ridgelet domain
    School of Science, Xidian Univ., Xi'an 710071, China
    不详
    Xi'an Dianzi Keji Daxue Xuebao, 2007, 4 (669-672):
  • [9] Image Denoising using Ridgelet Shrinkage
    Kumar, Pawan
    Bhurchandi, K. M.
    SIXTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2014), 2015, 9443
  • [10] A symmetric alternating minimization algorithm for total variation minimization
    Lei, Yuan
    Xie, Jiaxin
    SIGNAL PROCESSING, 2020, 176