High-order total variation-based image restoration

被引:637
|
作者
Chan, T
Marquina, A
Mulet, P
机构
[1] Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA
[2] Univ Valencia, Dept Matemat Aplicada, E-46100 Valencia, Spain
关键词
image denoising; total variation; fourth order PDE;
D O I
10.1137/S1064827598344169
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The total variation ( TV) denoising method is a PDE-based technique that preserves edges well but has the sometimes undesirable staircase effect, namely, the transformation of smooth regions ( ramps) into piecewise constant regions ( stairs). In this paper we present an improved model, constructed by adding a nonlinear fourth order diffusive term to the Euler-Lagrange equations of the variational TV model. Our technique substantially reduces the staircase effect, while preserving sharp jump discontinuities ( edges). We show numerical evidence of the power of resolution of this novel model with respect to the TV model in some 1D and 2D numerical examples.
引用
收藏
页码:503 / 516
页数:14
相关论文
共 50 条
  • [21] Independent Gabor analysis of multiscale total variation-based quotient image
    An, Gaoyun
    Wu, Jiying
    Ruan, Qiuqi
    IEEE SIGNAL PROCESSING LETTERS, 2008, 15 : 186 - 189
  • [22] Total variation-based image noise reduction with generalized fidelity function
    Lee, Suk-Ho
    Kang, Moon Gi
    IEEE SIGNAL PROCESSING LETTERS, 2007, 14 (11) : 832 - 835
  • [23] Few-view image reconstruction combining total variation and a high-order norm
    Zhang, Yi
    Zhang, Wei-Hua
    Chen, Hu
    Yang, Meng-Long
    Li, Tai-Yong
    Zhou, Ji-Liu
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2013, 23 (03) : 249 - 255
  • [24] AVO inversion with high-order total variation regularization
    She, Bin
    Wang, Yaojun
    Zhang, Jiashu
    Wang, Jinduo
    Hu, Guangmin
    JOURNAL OF APPLIED GEOPHYSICS, 2019, 161 : 167 - 181
  • [25] Contrast Enhancement via Dual Graph Total Variation-Based Image Decomposition
    Liu, Xianming
    Zhai, Deming
    Bai, Yuanchao
    Ji, Xiangyang
    Gao, Wen
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 30 (08) : 2463 - 2476
  • [26] Adaptive total variation and second-order total variation-based model for low-rank tensor completion
    Xin Li
    Ting-Zhu Huang
    Xi-Le Zhao
    Teng-Yu Ji
    Yu-Bang Zheng
    Liang-Jian Deng
    Numerical Algorithms, 2021, 86 : 1 - 24
  • [27] Adaptive total variation and second-order total variation-based model for low-rank tensor completion
    Li, Xin
    Huang, Ting-Zhu
    Zhao, Xi-Le
    Ji, Teng-Yu
    Zheng, Yu-Bang
    Deng, Liang-Jian
    NUMERICAL ALGORITHMS, 2021, 86 (01) : 1 - 24
  • [28] Hybrid High-Order and Fractional-Order Total Variation with Nonlocal Regularization for Compressive Sensing Image Reconstruction
    Hou, Lijia
    Qin, Yali
    Zheng, Huan
    Pan, Zemin
    Mei, Jicai
    Hu, Yingtian
    ELECTRONICS, 2021, 10 (02) : 1 - 17
  • [29] Clinical low dose CT image reconstruction using high-order total variation techniques
    Do, Synho
    Karl, Clem
    Kalra, Mannudeep K.
    Brady, Thomas J.
    Pien, Homer
    MEDICAL IMAGING 2010: PHYSICS OF MEDICAL IMAGING, 2010, 7622
  • [30] Spatially dependent regularization parameter selection for total generalized variation-based image denoising
    Tian-Hui Ma
    Ting-Zhu Huang
    Xi-Le Zhao
    Computational and Applied Mathematics, 2018, 37 : 277 - 296