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Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse i ...
In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...
In this paper, we address the problemof super-resolution from multiple low-resolution omnidirectional images with inexact registration. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect ...
In recent years, many works on geometric image representation have appeared in the literature. Geometric video representation has not received such an important attention so far, and only some initial works in the area have been presented. Works on geometr ...
A major limitation of thermal therapies is the lack of detailed thermal information needed to monitor the therapy. Temperatures are routinely measured invasively with thermocouples, but only sparse measurements can be made. Ultrasound tomography is an attr ...
Compressed sensing (CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse background-subtracted silhouettes and show the usefulness of such an approach ...
We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. A general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requir ...
Compressive Sensing (CS) combines sampling and compression into a single sub-Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a g ...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal ...
This paper addresses the reconstruction of high resolution omnidirectional images from multiple low resolution images with inexact registration. When omnidirectional images from low resolution vision sensors can be uniquely mapped on the 2-sphere, such a r ...