We study the problem of selecting the best sampling set for bandlimited reconstruction of
signals on graphs. A frequency domain representation for graph signals can be defined …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Large-scale smart energy metering deployment worldwide and integration of smart meters
within the smart grid will enable two-way communication between the consumer and energy …

Geometric data acquired from real-world scenes, eg, 2D depth images, 3D point clouds, and
4D dynamic point clouds, have found a wide range of applications including immersive …

In this paper we propose a generic recursive algorithm for improving image denoising
methods. Given the initial denoised image, we suggest repeating the following “SOS” …

We consider a class of popular distributed non-convex optimization problems, in which
agents connected by a network ς collectively optimize a sum of smooth (possibly non …

This paper presents a graph signal denoising method with the trilateral filter defined in the
graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is …

In this paper, we present a novel generalization of the graph Fourier transform (GFT). Our
approach is based on separately considering the definitions of signal energy and signal …

We propose a method for graph learning from spatiotemporal measurements. We aim at
inferring time-varying graphs under the assumption that changes in graph topology and …