The General sampling theorem, Compressed sensing and a method of image sampling and reconstruction with sampling rates close to the theoretical limit

TL;DR

This paper reviews recent advances in sampling theory, compressed sensing, and introduces a practical image sampling and reconstruction method that approaches the theoretical minimal sampling rate.

Contribution

It presents a new practical method for image sampling and reconstruction that operates near the theoretical sampling limit, building on recent theoretical developments.

Findings

Sampling rates close to the theoretical minimum achieved

Compressed sensing potentials and limitations analyzed

A practical sampling and reconstruction method demonstrated

Abstract

The article addresses the problem of image sampling with minimal possible sampling rates and reviews the recent advances in sampling theory and methods: modern formulations of the sampling theorems, potentials and limitations of Compressed sensing methods and a practical method of image sampling and reconstruction with sampling rates close to the theoretical minimum.