Journal of Zhejiang University SCIENCE C

, Volume 15, Issue 8, pp 664–674 | Cite as

Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

  • Nu WenEmail author
  • Shi-zhi Yang
  • Cheng-jie Zhu
  • Sheng-cheng Cui


In this paper, we present an adaptive two-step contourlet-wavelet iterative shrinkage/thresholding (TcwIST) algorithm for remote sensing image restoration. This algorithm can be used to deal with various linear inverse problems (LIPs), including image deconvolution and reconstruction. This algorithm is a new version of the famous two-step iterative shrinkage/thresholding (TwIST) algorithm. First, we use the split Bregman Rudin-Osher-Fatemi (ROF) model, based on a sparse dictionary, to decompose the image into cartoon and texture parts, which are represented by wavelet and contourlet, respectively. Second, we use an adaptive method to estimate the regularization parameter and the shrinkage threshold. Finally, we use a linear search method to find a step length and a fast method to accelerate convergence. Results show that our method can achieve a signal-to-noise ratio improvement (ISNR) for image restoration and high convergence speed.

Key words

Image restoration Adaptive Cartoon-texture decomposition Linear search Iterative shrinkage/thresholding 

CLC number



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Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nu Wen
    • 1
    • 2
    • 3
    Email author
  • Shi-zhi Yang
    • 1
    • 2
  • Cheng-jie Zhu
    • 1
    • 2
    • 3
  • Sheng-cheng Cui
    • 1
    • 2
  1. 1.Anhui Institute of Optics and Fine MechanicsChinese Academy of SciencesHefeiChina
  2. 2.Key Laboratory of Optical Calibration and CharacterizationChinese Academy of SciencesHefeiChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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