Compressive image sensing for fast recovery from limited samples: A variation on compressive sensing

摘要

In order to attain better reconstruction quality from compressive sensing (CS) of images, exploitation of the dependency or correlation patterns among the transform coefficients commonly has been employed. In this paper, we study a new image sensing technique, called compressive image sensing (CIS), with computational complexity O(m(2)), where m denotes the length of a measurement vector y = phi x, which is sampled from the signal x of length n via the sampling matrix phi with dimensionality m x n. CIS is basically a variation on compressive sampling.