Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

Using a novel characterization of texture, we propose an image decomposition technique
that can effectively decomposes an image into its cartoon and texture components. The …

This paper proposes harmonic vector analysis (HVA) based on a general algorithmic
framework of audio blind source separation (BSS) that is also presented in this paper. BSS …

Stochastic approximation techniques play an important role in solving many problems
encountered in machine learning or adaptive signal processing. In these contexts, the …

This paper addresses the selection of a desirable solution among all the solutions of a
convex optimization problem (referred to as the first-stage problem) mainly for inverse …

Stochastic approximation techniques have been used in various contexts in data science.
We propose a stochastic version of the forward-backward algorithm for minimizing the sum …

We propose an efficient multikernel adaptive filtering algorithm with double regularizers,
providing a novel pathway towards online model selection and learning. The task is the …

A new denoising algorithm based on orthogonal projections onto the epigraph set of a
convex cost function is presented. In this algorithm, the dimension of the minimization …

Observing that a typical primary path in Active Noise Control (ANC) system is sparse, ie,
having a few significant coefficients, we propose an adaptive learning which promotes the …