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Adaptive Noise Variance Estimation in BayesShrink

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2 Author(s)
Masoud Hashemi ; Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada ; Soosan Beheshti

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.

Published in:

IEEE Signal Processing Letters  (Volume:17 ,  Issue: 1 )