Abstract
Super-resolution applications require sub-pixel registrations of low resolution images to be almost exact due to the deterioration caused by inaccurate image registration. A linear-least-squares technique is proposed to refine sub-pixel translation parameters, which can be employed when the images are registered but just where there is not enough sub-pixel accuracy. In the technique, it is assumed that low resolution pixels are obtained by area sampling high resolution pixel field which have twice the density of their low resolution correspondents. Using this downsampling schema, a set of equations is formed. Assumed geometry and layout provide a constraint set to be used with the equation set. The sub-pixel translations are then found using least-squares-solution-with-equality-constraints. The method is shown to improve the registration accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
S.C. Park, M.K. Park, and M.G. Kang, “Super-resolution image reconstruction: A technical overview,” IEEE Signal Processing Magazine, May 2003, pp. 21–36.
S. Borman and R.L. Stevenson, “Super-resolution from image sequences—A review,” Proceedings of the 1998 Midwest Symposium on Circuits and Systems, Notre Dame, IN, 1998.
B. Zitova and S. Flusser, “Image registration methods: A survey,” Elsevier Image and Vision Computing, Vol. 21, pp. 977–1000, 2003.
P. Chalermwat, “High performance automatic image registration for remote sensing,” PhD Dissertation, George Mason University, Fairfax, Virginia, 1999.
A. Aldroubi, “Non-uniform weighted average sampling and reconstruction in shift-invariant and wavelet spaces,” Applied and Computational Harmonic Analysis, Vol. 13, pp. 151–161, 2002.
M. Irani and S. Peleg, “Improving resolution by image registration,” Computer Vision, Graphics and Image Processing, Vol. 53, pp. 231–239, May 1991.
H.S. Stone, M. Orchard, and E.-C. Chang, “Sub-pixel registration of images,” in Proc. Asilomar Conf. Signals, Systems, and Computers, October 1999.
H. Foroosh, J.B. Zerubia, and M. Berthod, “Extension of phase correlation to sub-pixel registration,” IEEE Transactions on Image Processing, Vol. 11, No. 3, pp. 188–200, 2002.
D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Transactions on Image Processing, Vol. 13, No. 9, pp. 1185–1199, 2004.
P. Vandewalle, S. Süsstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP Journal on Applied Signal Processing (special issue on Super-resolution Imaging), 2005.
Author information
Authors and Affiliations
Corresponding author
Additional information
Erol Seke received B.S. and M.S. degrees in electrical engineering from Anadolu University, Eskisehir, Turkey, in 1985 and 1987 respectively, Ph.D. degree in electrical engineering from Lehigh University, Bethlehem, PA in 1995. He is currently an assistant professor in the Dept. of Electrical & Electronics Eng. at Eskisehir Osmangazi University, Turkey. His primary research interest is in image processing.
Kemal Özkan was born on March, 17, 1975, in Antalya, Turkey. He recieved B.S. and M.S. degrees in electrical & electronics engineering from Eskisehir Osmangazi University, Turkey, in 1998 and 2000, respectively where he is currently a Ph.D. student. His current research interests include digital image and video processing.
Rights and permissions
About this article
Cite this article
Seke, E., Özkan, K. Least Squares Sub-pixel Registration Refinement Using Area Sampler Model. J Math Imaging Vis 26, 19–25 (2006). https://doi.org/10.1007/s10851-006-3600-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-006-3600-3