Reviews a significant component of the rich field of statistical multiresolution (MR) modeling
and processing. These MR methods have found application and permeated the literature of …

In this paper, we discuss the compression of waveforms obtained from measurements of
power system quantities and analyze the reasons why its importance is growing with the …

We propose a robust wavelet domain method for noise filtering in medical images. The
proposed method adapts itself to various types of image noise as well as to the preference of …

This paper introduces a new class of bases, called bandelet bases, which decompose the
image along multiscale vectors that are elongated in the direction of a geometric flow. This …

For extracting a signal from noisy data, waveshrink and basis pursuit are powerful tools both
from an empirical and asymptotic point of view. They are especially efficient at estimating …

The sparseness and decorrelation properties of the discrete wavelet transform have been
exploited to develop powerful denoising methods. However, most of these methods have …

The nonparametric multiscale platelet algorithms presented in this paper, unlike traditional
wavelet-based methods, are both well suited to photon-limited medical imaging applications …

A compressive sensing (CS) approach for nonstationary signal separation is proposed. This
approach is motivated by challenges in radar signal processing, including separations of …

'The so-called denoising problem, relative to normal models for noise, is formalized such
that" noise" is defined as the incompressible part in the data while the compressible part …

Nonlinear filtering techniques based on the theory of robust estimation are introduced. Some
deterministic and asymptotic properties are derived. The proposed denoising methods are …