Title :
Adaptive Noise Variance Estimation in BayesShrink
Author :
Hashemi, Masoud ; Beheshti, Soosan
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
A method of noise variance estimation in BayesShrink image denoising is presented. The proposed approach competes with the well known MAD-based method and outperforms this method in more than 99% of our experimental results. The approach, called Residual Autocorrelation Power (RAP), provides a more accurate noise variance estimate and results in a smaller MSE.
Keywords :
Bayes methods; adaptive estimation; correlation methods; image denoising; mean square error methods; BayesShrink image denoising; MAD-based method; adaptive noise variance estimation; mean square error; median absolute deviation; residual autocorrelation power; BayesShrink image denoising; Median Absolute Deviation (MAD); noise variance estimation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2009.2030856
