References
- Guo K., Lim W., Labate D., Weiss G. and Wilson E. Wavelets with composite dilations and their MRA properties. Appl. Comput. Harmon. Anal., 2006, 20, 231–249.
- Mallat S. A Wavelet Tour of Signal Processing, 1998 (Academic, New York).
- Starck J. L., Candès E. J. and Donoho D. L. The curvelet transform for image denoising. IEEE Trans. Image Process., 2002, 11, 670–684.
- Candès E. and Donoho D. In Curves and Surfaces (Ed. A. Cohen, C. Rabut and L. L. Schumaker), 1999, pp. 105–120 (Vanderbilt University Press, Nashville, TN).
- Candes E., Demanet L., Donoho D. and Ying L. X. Fast discrete curvelet transforms. Multiscale Model. Simul., 2006, 5, 861–899.
- Donoho D. L. Sparse components of images and optimal atomic decomposition. Constr. Approx., 2001, 17, 353–382.
- Hennenfent G., Herrmann F. and Neelamani R. Seismic deconvolution revisited with curvelet frames, Proc. EAGE 67th Conf. and Exhib., Madrid, Spain, June 2005, EAGE, 4255–4258.
- Candès E. J. and Donoho D. L. Recovering edges in ill-posed inverse problems: optimality of curvelet frames. Ann. Statist., 2002, 30, 784–842.
- Buades A., Coll B. and Morel J. M. A non-local algorithm for image denoising, Proc. IEEE Int. Conf. on Computer vision and pattern recognition: CVPR 2005, San Diego, CA, USA, June 2005, IEEE Computer Society, pp. 60–65.
- Buades A., Coll B. and Morel J. M. Nonlocal image and movie denoising. Int. J. Comput. Vis., 2008, 76, 123–139.
- Katsaggelos A. K. (Ed.) Digital Image Restoration, 1991 (Springer-Verlag, New York).
- Hansen P. C. Rank-deficient and Discrete Ill-posed Problems: Numerical Aspects of Linear Inversion, 1998 (SIAM, Philadelphia, PA).
- Donoho D. L. Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition. Appl. Comput. Harmon. Anal., 1995, 2, 101–126.
- Neelamani R., Choi H. and Baraniuk R. G. ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Trans. Signal Process., 2004, 52, 418–433.
- Patel V. M., Easley G. R. and Healy D. M. Jr Shearlet-based deconvolution. IEEE Trans. Image Process., 2009, 18, 2673–2685.
- Yang H. and Zhang Z. B. Image deblurring based on ForIcM: Fourier shrinkage and incomplete measurements. J. Imaging Sci., 2012, 60, 344–351.
- Portilla J. Image restoration through L0 analysis-based sparse optimization in tight frames, Proc. 16th IEEE Int. Conf. on Image processing: ICIP 2009, Cairo, Egypt, November 2009, IEEE, pp. 3909–3912.
- Chantas G., Galatsanos N., Molina R. and Katsaggelos A. Variational bayesian image restoration with a product of spatially weighted total variation image priors. IEEE Trans. Image Process., 2010, 19, 351–362.
- Gan X. C., Liew A. W.-C. and Yan H. A POCS-based constrained total least squares algorithm for image restoration. J. Vis. Commun. Image Represent., 2006, 17, 986–1003.
- Landweber L. An iterative formula for Fredholm integral equations of the first kind. Am. J. Math., 1951, 73, 615–624.
- Richardson W. H. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am., 1972, 62, 55–59.
- Lucy L. B. An iterative technique for the rectification of observed distributions. Astron. J., 1974, 79, 745–754.
- Do M. N. and Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process., 2005, 14, 2091–2106.
- Dong W. S., Zhang L., Shi G. M. and Wu X. L. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process., 2011, 20, 1838–1857.
- Wang Y., Yang J., Yin W. and Zhang Y. A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imag. Sci., 2008, 1, 248–272.
- Beck A. and Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci., 2009, 2, 183–202.
- Bioucas-Dias J. and Figueiredo M. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans. Imaging Process., 2007, 16, 2992–3004.
- Michailovich O. V. An iterative shrinkage approach to total-variation image restoration. IEEE Trans. Image Process., 2011, 20, 1281–1299.
- Hillery A. D. and Chin R. T. Iterative Wiener filters for image restoration. IEEE Trans. Signal Process., 1991, 39, 1892–1899.
- Ghael S., Sayeed A. M. and Baraniuk R. G. Improved wavelet denoising via empirical Wiener filtering. Proc. SPIE, 1997, 3169, 389–399.
- Zhao M., Zhang W. and Wang Z. L. Satellite image deconvolution based on nonlocal means. Appl. Opti., 2010, 49, 6286–6294.
- Petschnigg G., Agrawala M., Hoppe H., Szeliski R., Cohen M. and Toyoma K. Digital photography with flash and no-flash image pairs. ACM Trans. Graph., 2004, 23, 664–672.
- Vignesh R., Oh B. T. and Kuo C.-C. J. Fast non-local means (NLM) computation with probabilistic early termination. IEEE Signal Process. Lett., 2010, 17, 277–280.
- Wong A., Fieguth P. and Clausi D. A perceptually adaptive approach to image denoising using anisotropic non-local means, Proc. 15th IEEE Int. Conf. on Image processing: ICIP 2008, San Diego, CA, USA, October 2008, IEEE, pp. 537–540.