BOAZ COHEN^* and ITS’HAK DINSTEIN

Electrical and Computer Engineering Department,

Ben-Gurion University of the Negev,

P.O. Box 653, Beer-Sheva 84105, Israel.

Email- {boazc, dinstein}@ee.bgu.ac.il

Abstract-

When performing block-matching based motion estimation with the ML estimator, one would try to match blocks from the two images, within a predefined search area. The estimated motion vector is the one maximizing a likelihood function, formulated according to the image formation model. Two new maximum likelihood motion estimation schemes for ultrasound images are presented. The new likelihood functions are based on the assumption that both images are contaminated by a Rayleigh distributed multiplicative noise. The new approach enables motion estimation in cases where a noiseless reference image is not available. Experimental results show a motion estimation improvement with regards to other known ML estimation methods.