Subpixel registration of images

Phase correlation has been studied as a tool for registering pairs of images to subpixel resolution. Shekarforoush et al. (1994) investigated an algorithm that works well for when observed data are samples of ideally bandlimited images. In fact, images of real scenes captured by modern optics are not ideally bandlimited images. This paper examines how aliasing in sampled images effects the ideal phase relationships between continuous images undergoing translational shifts. We show that the performance of standard phase correlation methods for subpixel image registration can be severely degraded with moderate amounts of aliasing, and we investigate two approaches for modeling aliasing effects in order to improve subpixel image registration accuracy. The first approach, a very simple algorithm, both conceptually and computationally is based on detecting those frequency components that have become unreliable estimators of shift due to aliasing, and removing the detected components from the shift estimate. The second approach, more ambitious and more complex, attempts to undo the effects of aliasing and to use all de-aliased frequency components in the shift estimator.

[1]  Harold S. Stone,et al.  Progressive wavelet correlation using Fourier methods , 1999, IEEE Trans. Signal Process..

[2]  Michael Unser,et al.  A pyramid approach to subpixel registration based on intensity , 1998, IEEE Trans. Image Process..

[3]  John A. Stuller,et al.  New perspectives for maximum likelihood time-delay estimation , 1997, IEEE Trans. Signal Process..

[4]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[5]  Josiane Zerubia,et al.  Subpixel image registration by estimating the polyphase decomposition of cross power spectrum , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Ingemar J. Cox,et al.  Target testing and the PicHunter Bayesian multimedia retrieval system , 1996, Proceedings of the Third Forum on Research and Technology Advances in Digital Libraries,.

[7]  Nader M. Namazi,et al.  A New Image Motion Estimation Algorithm Based on the EM Technique , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  B N Chatterji,et al.  Fourier-Mellin Transform based Image Matching Algorithm , 1996 .

[9]  Gian Antonio Mian,et al.  On the registration of an object translating on a static background , 1996, Pattern Recognit..

[10]  Marco Corvi,et al.  Multiresolution image registration , 1995, Proceedings., International Conference on Image Processing.

[11]  Michel Defrise,et al.  Symmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognition , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  T. J. Dennis,et al.  Stereo disparity analysis using phase correlation , 1994 .

[13]  C. R. Guarino A novel method for two-dimensional phase estimation , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Michael Werman,et al.  Reconstruction of high resolution 3D visual information , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ernest M. Stokely,et al.  Laplacian and orthogonal wavelet pyramid decompositions in coarse-to-fine registration , 1993, IEEE Trans. Signal Process..

[16]  A. Bijaoui,et al.  Geometrical registration of images: the multiresolution approach , 1993 .

[17]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[18]  Q. Zheng,et al.  A computational vision approach to image registration , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[19]  N. Namazi,et al.  Variable time delay estimation in colored and correlated noise environment , 1992 .

[20]  Arthur P. Cracknell,et al.  Pixel and sub-pixel accuracy in geometrical correction of AVHRR imagery , 1989 .

[21]  Shmuel Peleg,et al.  Image sequence enhancement using sub-pixel displacements , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  John A. Stuller,et al.  A new approach to signal registration with the emphasis on variable time delay estimation , 1987, IEEE Trans. Acoust. Speech Signal Process..

[23]  C. Morandi,et al.  Registration of Translated and Rotated Images Using Finite Fourier Transforms , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Stefano Alliney,et al.  Digital Image Registration Using Projections , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Stefano Alliney Digital Analysis Of Rotated Images , 1985, Medical Imaging.

[26]  N. Bershad,et al.  Time delay estimation using the LMS adaptive filter--Dynamic behavior , 1981 .

[27]  D. Etter,et al.  Adaptive estimation of time delays in sampled data systems , 1981 .

[28]  R. Wong,et al.  Scene matching with invariant moments , 1978 .

[29]  C. D. Kuglin,et al.  The phase correlation image alignment method , 1975 .

[30]  Steven A. Tretter,et al.  Optimum processing for delay-vector estimation in passive signal arrays , 1973, IEEE Trans. Inf. Theory.

[31]  P. E. Anuta,et al.  Spatial Registration of Multispectral and Multitemporal Digital Imagery Using Fast Fourier Transform Techniques , 1970 .