Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …

The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …

Implicit neural representations (INRs) have recently advanced numerous vision-related
areas. INR performance depends strongly on the choice of activation function employed in …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

The goal of compressed sensing is to estimate a vector from an underdetermined system of
noisy linear measurements, by making use of prior knowledge on the structure of vectors in …

The goal of this paper is to present a non-iterative and more importantly an extremely fast
algorithm to reconstruct images from compressively sensed (CS) random measurements. To …