This paper focuses on developing signal reconstruction algorithms from Cauchy random projections, where the large suite of reconstruction algorithms developed in compressive sensing perform poorly due to the lack of finite second-order statistics in the projections.Expand

We establish a general framework, along with a set of algorithms, for the incorporation of the Structural Similarity (SSIM) quality index measure as the fidelity, or “data fitting,” term in objective functions for optimization problems in image processing.Expand

In a few words, this thesis is concerned with two alternative approaches to imaging, namely, Function-valued Mappings (FVMs) and Structural Similarity Index Measure (SSIM)-based Optimization.Expand

We proposed two algorithms to reconstruct a sparse signal from dimensionality reduced sketches in that are obtained using Cauchy random projections using explicit tail bounds for the geometric mean.Expand

Many tasks in image processing are carried out by solving appropriate optimization problems. As is well known, the square of the Euclidian distance is widely used as a fitting term, even though it… Expand

In this paper we propose a new method of solving optimization problems involving the structural similarity image quality measure with \(L^1\)-regularization.Expand

A formulation of hyperspectral images as function-valued mappings is introduced, along with a set of simple models of affine self-similarity for digital hyperspectrals.Expand