By Topic

A novel memory-efficient fast algorithm for 2-D compressed sensing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Huiyuan Wang ; IEETA, Departamento de Electrónica, Telecomunicações e Informática, Universidade de Aveiro, 3810-193, Portugal ; José Vieira ; Bruno Jesus

The 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.

Published in:

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Date of Conference:

7-9 July 2010