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 structur...

Bayesian wavelet shrinkage methods are defined through a prior distribution on the space of wavelet coefficients after a Discrete Wavelet Transformation has been applied to the data. Posterior summaries of the wavelet coefficients establish a Bayes shrinkage rule. After the Bayes shrinkage is performed, an Inverse Discrete Wavelet Transformation can be used to recover the signal that generated ...

The ` minimization of compressed sensing is often relaxed to `, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original ` penalty and empirically can recover the ori...

Thresholding rules recently became of considerable interest when Donoho and John-stone applied them in the wavelet shrinkage context. Analytically simple, such rules are very eecient in data denoising and data compression problems. In this paper we nd hard thresholding decision rules that minimize Bayes risk for broad classes of underlying models. Standard Donoho-Johnstone test signals are used...

For many natural signals, the wavelet transform is a more effective tool than the Fourier transform. The wavelet transform provides a multi resolution representation using a set of analyzing functions that are dilations and translations of a few functions. The wavelet transform lacks the shift-invariance property, and in multiple dimensions it does a poor job of distinguishing orientations, whi...