2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

非下采样轮廓波变换快速算法

严春满 郭宝龙 易盟

严春满, 郭宝龙, 易盟. 非下采样轮廓波变换快速算法. 自动化学报, 2014, 40(4): 757-762. doi: 10.3724/SP.J.1004.2014.00757
引用本文: 严春满, 郭宝龙, 易盟. 非下采样轮廓波变换快速算法. 自动化学报, 2014, 40(4): 757-762. doi: 10.3724/SP.J.1004.2014.00757
YAN Chun-Man, GUO Bao-Long, YI Meng. Fast Algorithm for Nonsubsampled Contourlet Transform. ACTA AUTOMATICA SINICA, 2014, 40(4): 757-762. doi: 10.3724/SP.J.1004.2014.00757
Citation: YAN Chun-Man, GUO Bao-Long, YI Meng. Fast Algorithm for Nonsubsampled Contourlet Transform. ACTA AUTOMATICA SINICA, 2014, 40(4): 757-762. doi: 10.3724/SP.J.1004.2014.00757

非下采样轮廓波变换快速算法

doi: 10.3724/SP.J.1004.2014.00757

Fast Algorithm for Nonsubsampled Contourlet Transform

Funds: 

Supported by National Natural Science Foundation of China (61367005, 61162017) and the Fundamental Research Funds for the Universities of Gansu Province

  • 摘要: 多尺度几何分析(MGA)是一种有效的图像处理方法. 作为MGA的一种离散实现方法,非下采样轮廓波变换(NSCT)被广泛应用于图像去噪、图像融合、图像增强、特征提取等领域. 然而,由于该变换的高冗余性,其计算效率受到一定限制. 因此,对NSCT快速算法的研究具有现实意义. 本文采用一种优化的方向滤波器改进了原NSCT变换,以损失部分重建图像质量为代价,获得算法处理速度的显著提高. 实验结果可见,在满足重建图像主观质量视觉要求的前提下,算法速度可比原变换提高若干倍. 图像去噪实验进一步验证了算法的可靠性及效率.
  • [1] Meyer F G, Coifman R R. Brushlets: a tool for directional image analysis and image compression. Applied and Computational Harmonic Analysis, 1997, 4(2): 147-187
    [2] Le Pennec E, Mallat S. Sparse geometric image representations with Bandelets. IEEE Transactions on Image Processing, 2005, 14(4): 423-438
    [3] Velisavljevic V, Beferull-Lozano B, Vetterli M, Dragotti P L. Directionlets: anisotropic multidirectional representation with separable filtering. IEEE Transactions on Image Processing, 2006, 15(7): 1916-1933
    [4] Candés E J. Ridgelets: Theory and Application [Ph.D. dissertation], Stanford University, Stanford CA, 1998
    [5] Candés E J, Donoho D L. Curvelets——a surprisingly effective nonadaptive representation for objects with edges. Curves and Surfaces, Nashville, TN: Vanderbilt University Press, 2000. 105-120
    [6] Strack J L, Candés E J, Donoho D L. The curvelet transform for image denoising. IEEE Transactions on Image Processing, 2002, 11(6): 670-684
    [7] Candés E J, Demanet L, Donoho D L, Ying L X. Fast discrete curvelet transforms. Multiscale Modeling and Simulation, 2006, 5(3): 861-899
    [8] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106
    [9] Do M N. Directional Multiresolution Image Representations [Ph.D. dissertation], Lausanne Federal Polytechnic School, Lausanne, Swiss, 2001
    [10] da Cunha A L, Zhou J P, Do M N. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101
    [11] Zhou H F, Wang X T, Xu X G. Image denoising using Gaussian scale mixture model in the nonsubsampled contourlet domain. Journal of Electronics and Information Technology, 2009, 31(8): 1796-1800
    [12] Feng H X, Hou B, Jiao L C, Bu X M. SAR image despeckling based on local Gaussian model and MAP in NSCT domain. Acta Electronica Sinica, 2010, 38(4): 811-816
    [13] Qu X B, Yan J W, Xiao H Z, Zhu Z Q. Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Automatica Sinica, 2008, 34(12): 1508-1514
    [14] Li T J, Wang Y Y. Biological image fusion using a NSCT based variable-weight method. Information Fusion, 2011, 12(2): 85-92
    [15] Bamberger R H, Smith M J T. A filter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing, 1992, 40(4): 882-893
    [16] Chen Y, Adams M D, Lu W S. Design of optimal quincunx filter banks for image coding. Journal on Advances in Signal Processing, 2007, 2007: 083858
    [17] Sweldens W. The lifting scheme: a custom-design construction of biorthogonal wavelets. Applied and Computational Harmonic Analysis, 1996, 3(2): 186-200
    [18] Tran T D, de Queiroz R L, Nguyen T Q. Linear-phase perfect reconstruction filter bank: lattice structure, design, and application in image coding. IEEE Transactions on Signal Processing, 2000, 48(1): 133-147
    [19] Portilla J, Strela V, Wainwright M J, Simoncelli E P. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing, 2003, 12(11): 1338-1351
  • 加载中
计量
  • 文章访问数:  3475
  • HTML全文浏览量:  133
  • PDF下载量:  1523
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-01-24
  • 修回日期:  2013-05-24
  • 刊出日期:  2014-04-20

目录

    /

    返回文章
    返回