Deconvolution in a ridgelet and curvelet domain

Glenn R. Easley; Carlos A. Berenstein; Dennis M. Healy Jr.

doi:10.1117/12.602822

28 March 2005 Deconvolution in a ridgelet and curvelet domain

Glenn R. Easley, Carlos A. Berenstein, Dennis M. Healy Jr.

Proceedings Volume 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III; (2005) https://doi.org/10.1117/12.602822
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

We present techniques for performing image reconstruction based on deconvolution in the Radon domain. To deal with a variety of possible boundary conditions, we work with a corresponding generalized discrete Radon transform in order to obtain projection slices for deconvolution. By estimating the projections using wavelet techniques, we are able to do deconvolution directly in a ridgelet domain. We also show how this method can be carried out locally, so that deconvolution can be done in a curvelet domain as well. These techniques suggest a whole new paradigm for developing deconvolution algorithms, which can incorporate leading deconvolution schemes. We conclude by showing experimental results indicating that these new algorithms can significantly improve upon current leading deconvolution methods.

Citation Download Citation

Glenn R. Easley, Carlos A. Berenstein, and Dennis M. Healy Jr. "Deconvolution in a ridgelet and curvelet domain", Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); https://doi.org/10.1117/12.602822

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available