Image De-noising Method Based on Nonparametric Adaptive Density Estimation in Ridgelet Domain

Candes E J. Ridgelet: Theory and applications. Ph. D. Thesis, Department of Statistics, Stanford University, 1998.[2]Candes E J, Donoho D L. Ridgelets: A key to higher-dimensional intermittency[J].Philosophical Trans. of the Royal Society of London Series A.1999, 357(1760):2495-2509[3]Do M N, Vetterli M. The finite ridgelet transform for image representation[J].IEEE Trans. on Image Processing.2003, 12(1):16-28[4]Do M N, Vetterli M. Image denoising using orthonormal finite ridgelet transform. Proc. of SPIE Conf. on Wavelet Applications in Signal and Image Processing, San Diego, 2000, 4119: 831-842.[5]Birge L, Massart P. From model selection to adaptive estimation. In Festschrift for Lucien Le Cam: Research Paper in Probability and Statistics. New York: Springer-Verlag, 1997: 55-88.[6]Le Cam L. Convergence of estimates under dimensionality restrictions[J].Annals of Statistics.1973, 1(1):38-53[7]Barron A R, Birge L, Massart P. Risk bounds for model selectionvia penalization[J].Probability Theory and Related Fields.1999, 113(3):301-413[8]Karlsson G, Vetterli M. Extension of finite length signal for sub-band coding[J].Signal Processing.1989, 17(2):161-166