Imaging spectroscopy involves the sensing of a large amount of spatial information across a
multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent …

This paper describes a recently created image database, TID2013, intended for evaluation
of full-reference visual quality assessment metrics. With respect to TID2008, the new …

Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many
applications, robust estimators are needed. Among the scenarios in which robust …

This paper provides statistical analysis for efficient detection of signal components when
missing data samples are present. This analysis is important for both the areas of L-statistics …

Coded aperture snapshot spectral imaging (CASSI) provides a mechanism for capturing a
3D spectral cube with a single shot 2D measurement. In many applications selective …

A new database of distorted color images called TID2013 is designed and described. In
opposite to its predecessor, TID2008, this database contains images with five levels of …

The L-estimate transforms and time-frequency representations are presented within the
framework of compressive sensing. The goal is to recover signal or local auto-correlation …

Greedy pursuit, which includes matching pursuit (MP) and orthogonal matching pursuit
(OMP), is an efficient approach for sparse approximation. However, conventional greedy …

Commonly employed reconstruction algorithms in compressed sensing (CS) use the L2
norm as the metric for the residual error. However, it is well-known that least squares (LS) …

Compressive sensing is a powerful sensing and reconstruction framework for recovering
high dimensional signals with only a handful of observations and for spectral imaging …