Wang, HuiyuanVieira, JoséJesus, BrunoDuarte, IsabelFerreira, Paulo2015-01-082015-01-082010-06978-1-4244-6712-9http://hdl.handle.net/10400.19/2502The basic theories of compressed sensing (CS) turn around the sampling and reconstruction of 1-D signals. To deal with 2-D signals (images), the conventional treatment is to convert them into1-D vectors. This has drawbacks, including huge memory demands and difficulties in the design and calibration of the optical imaging systems. As a result, in 2009 some researchers proposed the concept of compressed imaging (CI) with separable sensing operators. However, their work is only focused on the sampling phase. In this paper, we propose a scheme for 2-D CS that is memory- and computation-efficient in both sampling and reconstruction. This is achieved by decomposing the 2-D CS problem into two stages with the help of an intermediate image. The intermediate image is then solved by direct orthogonal linear transform and the original image is reconstructed by solving a set of 1-D l1-norm minimization sub-problems. The experimental results confirm the feasibility of the proposed scheme.engCompressed sensing2-D transformImage reconstructionImaging samplingjournal article10.1109/WCICA.2010.5553848