By Topic

Reply to "Comments on Geometric Deconvolution: A Meta-Algorithm for Limited View Computed Tomography"2

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)
Richard Gordon ; Departments of Botany, Radiology, and Electrical Engineering, University of Manitoba ; Atam P. Dhawan ; Rangaraj M. Rangayyan

We have developed two new "meta-algorithms" for computed tomography that give significantly improved images through deconvolution of the two-dimensional point spread function of standard, quasi-linear algorithms. In geometric deconvolution the projections of the point spread function provide the basis for a set of one-dimensional deconvolutions. In two-dimensional Wiener deconvolution, the two-dimensional point spread function is deconvoluted directly. The criticism that there is no data available for these deconvolutions is met here by showing that the "missing data" is partly provided by incorporation of a priori information, as is the practice in other superresolution work.

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:BME-32 ,  Issue: 3 )