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.
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