The dictionary-aided sparse regression (SR) approach has recently emerged as a promising
alternative to hyperspectral unmixing in remote sensing. By using an available spectral …

We investigate a compressive sensing framework in which the sensors introduce a distortion
to the measurements in the form of unknown gains. We focus on blind calibration, using …

Refractive index as one of the most important optical properties of materials is the key to
utilize refractive index elements. However, it is very difficult to measure or estimate refractive …

Compressed sensing (CS) is a concept that allows to acquire compressible signals with a
small number of measurements. As such, it is very attractive for hardware implementations …

With the rapid increase in signal frequency and bandwidth faced by communication and
radar systems, compressive sampling systems have received attention. However, these …

Signals and Images: Advances and Results in Speech, Estimation, Compression,
Recognition, Filtering, and Processing cohesively combines contributions from field experts …

The ubiquity of approximately sparse data has led a variety of communities to take great
interest in compressed sensing algorithms. Although these are very successful and well …

Radio interferometric arrays as imperfect instruments require calibration to correct for
instrumental gain and phase errors in observations. One point of calibration, our focus, is …

Acoustic sensing is at the heart of many applications, ranging from underwater sonar and
nondestructive testing to the analysis of noise and their sources, medical imaging, and …

This work studies the problem of blind sensor calibration (BSC) in linear inverse problems,
such as compressive sensing. It aims to estimate the unknown complex gains at each …