Author(s): Tamer Nabil
Article publication date: 2010-09-01
Vol. 28 No. 3 (yearly), pp. 163-169.
138
Keywords
Wavelettransform, image De-spechling, translation invariatiant, Bayesian, estimation
theory, estimation, ultrasound images.
Abstract
This paper proposes an adaptive threshold estimation method for de-noising in wavelet
domains merged with translation invariant de-noising. The sub-band shrink is computationally
more efficient and adaptive because the parameters required for estimating the threshold depend
on subband data. A new probability density function is proposed to model the statistics of wavelet
coefficients. The subband threshold is derived using Bayesian estimation theory and the new pdf.
Different shifts are used and applied to the noisy image in order to attain different estimates to the
unknown image and then linearly average the estimates. In speckle images, the noise content is
multiplicative. The proposed method is applied for speckle ultrasound images by using logarithmic
transformation. Experimental results on several test images are compared with various de-noising
techniques.