Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration
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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 wordsImage restoration Adaptive Cartoon-texture decomposition Linear search Iterative shrinkage/thresholding
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- Bioucas-Dias, J.M., Figueiredo, M.A.T., 2007b. Two-step algorithms for linear inverse problems with nonquadratic regularization. Proc. IEEE Int. Conf. on Image Processing, p.I-105–I-108. [doi:10.1109/ICIP.2007.4378902]Google Scholar
- Bioucas-Dias, J.M., Figueiredo, M.A.T., 2008. An iterative algorithm for linear inverse problems with compound regularizers. Proc. 15th IEEE Int. Conf. on Image Processing, p.685–688. [doi:10.1109/ICIP.2008.4711847]Google Scholar
- Bioucas-Dias, J.M., Figueiredo, M.A.T., Oliveira, J.P., 2006. Total variation-based image deconvolution: a majorization-minimization approach. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.II. [doi:10.1109/ICASSP.2006.1660479]Google Scholar
- Figueiredo, M.A.T., Bioucas-Dias, J.M., Afonso, M.V., 2009. Fast frame-based image deconvolution using variable splitting and constrained optimization. Proc. IEEE/SP 15th Workshop on Statistical Signal Processing, p.109–112. [doi:10.1109/SSP.2009.5278628]Google Scholar
- Gilles, J., Osher, S., 2011. Bregman Implementation of Meyer’s G-Norm for Cartoon+Textures Decomposition. UCLA CAM Report.Google Scholar
- Meyer, Y., 2001. Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: the Fifteenth Dean Jacqueline B. Lewis Memorial Lectures. American Mathematical Society Boston, MA, USA.Google Scholar
- Nowak, R.D., Figueiredo, M.A.T., 2001. Fast wavelet-based image deconvolution using the EM algorithm. Proc. 35th Asilomar Conf. on Signals, Systems and Computers, p.371–375. [doi:10.1109/ACSSC.2001.986953]Google Scholar
- Pan, H.J., Blu, T., 2011. Sparse image restoration using iterated linear expansion of thresholds. Proc. 18th IEEE Int. Conf. on Image Processing, p.1905–1908. [doi:10.1109/ICIP.2011.6115842]Google Scholar