An approach for speckle reduction and feature enhancement under a framework of multiscale wavelet analysis is presented. The advantages of both soft thresholding and hard thresholding wavelet shrinkage techniques are utilized to eliminate noise and preserve the sharpness of salient features. We integrate a method of wavelet shrinkage with nonlinear processing to enhance contrast within structures and along object boundaries. Feature restoration and enhancement are accomplished by modifying the gain of a signal's variational energy. In this study, we focus on multiplicative noise, such as speckle noise. We show that this algorithm is capable of enhancing features of interest, such as endocardial and epicardial boundaries in 2-D short-axis echocardiograms while at the same time reducing speckle. Speckle is modeled as multiplicative noise, and approximated by additive stationary Gaussian white noise on a logarithmic scale. In our algorithm, shrinkage of wavelet coefficients via soft thres..
