Denoising image via minimum variance bound Bayesian estimator
Xu Huang · 2001
In this paper, a wavelet denoising images together with minimum variance bound for Bayesian least-squares estimator is investigated. Closer to a realistic situation, and unlike previous methods used for Bayesian least-squares estimator, for the case discussed here it is not necessary to know the variance of the noise. The parameters relative to Bayesian least-squares estimators of the model built up are carefully discussed and least-squares estimator based on Cramer-Rao lower bound (CRLB) is then established. An example, an improved Bayesian estimator that is a natural extension of the Wiener solution together with wavelet denoising image, is presented to illustrate our discussion.