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

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. The contributions of CIS include: (i) reconstruction speed is extremely fast due to a closed-form solution being derived; (ii) certain reconstruction accuracy is preserved because significant components of x can be reconstructed with higher priority via an elaborately designed phi; and (iii) in addition to conventional 1D sensing, we also study 2D separate sensing to enable simultaneous acquisition and compression of large-sized images. (C) 2015 Elsevier Inc. All rights reserved.