Abstract

A new theory named compressed sensing for simultaneous sampling and compression of signals has been becoming popular in the communities of signal processing, imaging and applied mathematics. In this paper, we present improved/accelerated iterative curvelet thresholding methods for compressed sensing reconstruction in the fields of remote sensing. Some recent strategies including Bioucas-Dias and Figueiredo’s two-step iteration, Beck and Teboulle’s fast method, and Osher et al’s linearized Bregman iteration are applied to iterative curvelet thresholding in order to accelerate convergence. Advantages and disadvantages of the proposed methods are studied using the so-called pseudo-Pareto curve in the numerical experiments on single-pixel remote sensing and Fourier-domain random imaging.

Citations