Exploiting correlations in projection-based image registration

Matthew Sambora; Richard K. Martin

doi:10.1117/1.2957973

1 July 2008 Exploiting correlations in projection-based image registration

Matthew Sambora, Richard K. Martin

Author Affiliations +

Optical Engineering, Vol. 47, Issue 7, 077005 (July 2008). https://doi.org/10.1117/1.2957973

Abstract

Projection-based image registration algorithms use the sum of the pixel values along a given axis of an image to detect spatial changes in temporally separated images. These algorithms have been shown to be computationally efficient and effective for aligning temporally separated images and for visually detecting sensor motion. Registering images via projections has also been shown as a method for overcoming registration errors caused by the presence of fixed pattern noise. This work describes a method that exploits the statistical properties of images with significant local correlation to improve the performance of projection-based image registration algorithms. The algorithm is shown to operate in low signal-to-noise ratio (SNR) conditions and to significantly improve registration performance by as much as a factor of 5.5 in mean squared error over existing projection-based registration algorithms at a minimal computational cost.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Matthew Sambora and Richard K. Martin "Exploiting correlations in projection-based image registration," Optical Engineering 47(7), 077005 (1 July 2008). https://doi.org/10.1117/1.2957973

Published: 1 July 2008

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