Wavelet-Based Image Denoising via Doubly Local Wiener Filtering Using Directional Windows and Mathematical Morphology

Eom I K and Kim Y S. Wavelet-based denoising with nearlyarbitrarily shaped windows[J].IEEE Signal Processing Letters.2004, 11(12):937-940[2]Kazubek M. Wavelet domain image denoising bythresholding and Wiener filtering[J].IEEE Signal ProcessingLetters.2003, 10(11):324-326[3]Fan G and Xia X G. Image denoising using local contextualhidden Marcov model in the wavelet domain[J].IEEE SignalProcessing Letters.2001, 8(5):125-128[4]Mihk M K, Kozinsev I, and Ramchandran K, et al..Low-complexity image denoising based on statisticalmodeling of wavelet coefficients. IEEE Signal ProcessingLetters, 1999, 7(6): 300-303.[5]Chang S G, Yu B, and Vetterli M. Spatially adaptive waveletthresholding with context modeling for image denoising[J].IEEE Trans. on Image Processing.2000, 9(9):1522-1531[6]Sendur L and Selesnick I W. Bivariate shrinkage with localvariance estimation[J].IEEE Signal Processing Letters.2002,9(12):438-441[7]Ghael S P, Sayeed A M, and Baraniuk R G. Improved waveletdenoising via empirical Wiener filtering. Proceedings of SPIE,San Diego, 1997: 389-399.[8]Portilla J, Strela V, and Wainwright M J, et al.. Imagedenoising using scale mixtures of Gaussians in the waveletdomain[J].IEEE Trans. on Image Processing.2003, 12(11):1338-1351[9]Shui P L. Image denoising algorithm via doubly local wienerfiltering with directional windows in wavelet domain. IEEESignal Processing Letters, 2005, 10(12): 681-684.[10]Eom I K and Kim Y S. Spatially adaptive denoising based onmixture modeling and interscale dependencies of waveletcoefficients. IEEE Int. Conf. Neural Networks SignalProcessing. Nanjing, China, 2003: 14-17.[11]Gonzalez R C and Woods E W. Digital Image Processing.MA, Addison-Wesley, Reading, 1992: 519-560.