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Proceedings Paper

Efficient projection-based technique for registering images in the presence of fixed-pattern noise
Author(s): Stephen C. Cain; Majeed M. Hayat; Ernest E. Armstrong
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Paper Abstract

The problem of registering two successive video frames has traditionally been addressed with the 2D cross-correlation shift estimator. In this paper, a computationally efficient method of image registration is investigated that can achieve improved registration performance over the 2D cross- correlator in the presence of both fixed-pattern and temporal noise. This is accomplished by transforming both the current frame and the previous frame into two vector projections formed by accumulating pixel values across the rows and the columns of the array. The 2D cross-correlator requires three 2D Fourier transforms at the size of the image. In order to avoid the use of 2D Fourier transforms for large arrays, other shift estimation procedures have been developed that rely only on gradients between the two frames to infer the inter-frame shifts. Gradient-based techniques exhibit degraded performance in comparison to the 2D cross-correlator since the gradient operation amplifies noise. The projection-based estimator alleviates the computational burden of estimating shifts while improving the performance relative to the 2D cross-correlation shift estimator. In order to demonstrate the noise rejection capability of the projection-based shift estimator, a figure of merit is developed that reflects the signal-to-noise ratio for the two different shift estimation procedures. The relative performance of the 2D cross-correlation shift estimator and the projection-based shift estimator can be compared through their associated figures of merit. These two methods are also compared through computer simulation.

Paper Details

Date Published: 17 July 2000
PDF: 9 pages
Proc. SPIE 4044, Hybrid Image and Signal Processing VII, (17 July 2000); doi: 10.1117/12.391924
Show Author Affiliations
Stephen C. Cain, ITT (United States)
Majeed M. Hayat, Univ. of Dayton (United States)
Ernest E. Armstrong, OptiMetrics, Inc. (United States)


Published in SPIE Proceedings Vol. 4044:
Hybrid Image and Signal Processing VII
David P. Casasent; Andrew G. Tescher, Editor(s)

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