CLC number: TP7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-07-16
Cited: 2
Clicked: 9071
Nu Wen, Shi-zhi Yang, Cheng-jie Zhu, Sheng-cheng Cui. Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration[J]. Journal of Zhejiang University Science C, 2014, 15(8): 664-674.
@article{title="Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration",
author="Nu Wen, Shi-zhi Yang, Cheng-jie Zhu, Sheng-cheng Cui",
journal="Journal of Zhejiang University Science C",
volume="15",
number="8",
pages="664-674",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300377"
}
%0 Journal Article
%T Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration
%A Nu Wen
%A Shi-zhi Yang
%A Cheng-jie Zhu
%A Sheng-cheng Cui
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 8
%P 664-674
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300377
TY - JOUR
T1 - Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration
A1 - Nu Wen
A1 - Shi-zhi Yang
A1 - Cheng-jie Zhu
A1 - Sheng-cheng Cui
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 8
SP - 664
EP - 674
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300377
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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