Title :
An FFT implementation of the generalized maximum likelihood algorithm for image smoothing
Author :
Göksel, N. Sibel ; Namazi, Nader M.
Author_Institution :
Dept. of Electr. Eng., Michigan Technol. Univ., Houghton, MI, USA
Abstract :
Practical implementation of the generalized maximum-likelihood algorithm on noise-corrupted images becomes prohibitive when the covariance of the noise-free image is unavailable. By partitioning the image into locally Markovian sub-blocks with separable correlation coefficients, a covariance model is found that enables fast Fourier transform (FFT) processing. Simulations using real images are presented to characterize the algorithm´s applicability and noise-reduction performance
Keywords :
fast Fourier transforms; picture processing; probability; FFT implementation; covariance model; fast Fourier transform; generalized maximum likelihood algorithm; image smoothing; locally Markovian sub-blocks; noise-corrupted images; noise-reduction performance; partitioning; separable correlation coefficients; Anisotropic magnetoresistance; Computational modeling; Convergence; Convolution; Covariance matrix; Flowcharts; Kernel; Noise figure; Smoothing methods; Statistics;
Conference_Titel :
Circuits and Systems, 1990., Proceedings of the 33rd Midwest Symposium on
Conference_Location :
Calgary, Alta.
Print_ISBN :
0-7803-0081-5
DOI :
10.1109/MWSCAS.1990.140927
