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

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Abstract

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.

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Authors and Affiliations

  1. Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, 230031, China

    Nu Wen, Shi-zhi Yang, Cheng-jie Zhu & Sheng-cheng Cui

  2. Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei, 230031, China

    Nu Wen, Shi-zhi Yang, Cheng-jie Zhu & Sheng-cheng Cui

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Nu Wen & Cheng-jie Zhu

Authors
  1. Nu Wen
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  2. Shi-zhi Yang
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  3. Cheng-jie Zhu
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  4. Sheng-cheng Cui
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Correspondence to Nu Wen.

Additional information

Project supported by the National Science & Technology Pillar Program (No. 2011BAB01B03), the National Natural Science Foun-dation of China (No. 41305019), and the Anhui Provincial Natural Science Foundation (No. 1308085QD70)

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Wen, N., Yang, Sz., Zhu, Cj. et al. Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration. J. Zhejiang Univ. - Sci. C 15, 664–674 (2014). https://doi.org/10.1631/jzus.C1300377

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  • DOI: https://doi.org/10.1631/jzus.C1300377

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