Different from conventional imaging methods, which are based on the first-order field
correlation, ghost imaging (GI) obtains the image information through high-order mutual …

The explosion of computational imaging has seen the frontier of image processing move
past linear problems, like denoising and deblurring, and towards non-linear problems such …

This dedicated overview of optical compressive imaging addresses implementation aspects
of the revolutionary theory of compressive sensing (CS) in the field of optical imaging and …

Inverse problems with Poisson data arise in many photonic imaging modalities in medicine,
engineering and astronomy. The design of regularization methods and estimators for such …

This book demonstrates the concept of Fourier ptychography, a new imaging technique that
bypasses the resolution limit of the employed optics. In particular, it transforms the general …

Modern imaging methods rely strongly on Bayesian inference techniques to solve
challenging imaging problems. Currently, the predominant Bayesian computational …

We examine the theoretical properties of enforcing priors provided by generative deep
neural networks via empirical risk minimization. In particular we consider two models, one in …

In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …

A rank-r matrix X∈R^m*n can be written as a product UV^⊤, where U∈R^m*r and
V∈R^n*r. One could exploit this observation in optimization: eg, consider the minimization …

The present paper is a survey aiming at characterizing all solutions of the discrete phase
retrieval problem. Restricting ourselves to discrete signals with finite support, this problem …