Institute of Electrical and Electronic Engineers (IEEE)
IEEE Transactions on Image Processing
A computationally efficient method for image registration is investigated that can achieve an improved performance over the traditional two-dimensional (2-D) cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise. The method relies on transforming each image in the sequence of frames into two vector projections formed by accumulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional (1-D) cross-correlation-based estimator. While gradient-based shift estimation techniques are computationally efficient, they often exhibit degraded performance under noisy conditions in comparison to cross-correlators due to the fact that the gradient operation amplifies noise. The projection-based estimator, on the other hand, significantly reduces the computational complexity associated with the 2-D operations involved in traditional correlation-based shift estimators while improving the performance in the presence of temporal and spatial noise. To show the noise rejection capability of the projection-based shift estimator relative to the 2-D cross correlator, a figure-of-merit is developed and computed reflecting the signal-to-noise ratio (SNR) associated with each estimator. The two methods are also compared by means of computer simulation and tests using real image sequences.
Armstrong, Ernest; Cain, Stephen C.; and Hayat, Majeed M., "Projection-based image registration in the presence of fixed-pattern noise" (2001). Electrical and Computer Engineering Faculty Research and Publications. 519.
ADA Accessible Version
Accepted version. IEEE Transactions on Image Processing, Vol. 10, No. 12 (December 2001): 1860-1872. DOI. © 2001 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.
Majeed M. Hayatr was affiliated with the University of Dayton at the time of publication.