A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution

Super-resolution algorithms reconstruct a high-resolution image from a set of low-resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low-resolution images are undersampled and have aliasing artifacts, the performance of standard registration algorithms decreases. We propose a frequency domain technique to precisely register a set of aliased images, based on their low-frequency, aliasing-free part. A high-resolution image is then reconstructed using cubic interpolation. Our algorithm is compared to other algorithms in simulations and practical experiments using real aliased images. Both show very good visual results and prove the attractivity of our approach in the case of aliased input images. A possible application is to digital cameras where a set of rapidly acquired images can be used to recover a higher-resolution final image.

[1]  S. P. Kim,et al.  Subpixel accuracy image registration by spectrum cancellation , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Luca Lucchese,et al.  A noise-robust frequency domain technique for estimating planar roto-translations , 2000, IEEE Trans. Signal Process..

[3]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

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

[5]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[6]  Sabine Süsstrunk,et al.  Superresolution images reconstructed from aliased images , 2003, Visual Communications and Image Processing.

[7]  S. Chaudhuri,et al.  Multi-objective super resolution: concepts and examples , 2003, IEEE Signal Process. Mag..

[8]  Joshua Gluckman,et al.  Gradient field distributions for the registration of images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[9]  Andrew Zisserman,et al.  Computer vision applied to super resolution , 2003, IEEE Signal Process. Mag..

[10]  Michael T. Orchard,et al.  A fast direct Fourier-based algorithm for subpixel registration of images , 2001, IEEE Trans. Geosci. Remote. Sens..

[11]  A. Papoulis,et al.  Generalized sampling expansion , 1977 .

[12]  A. Murat Tekalp,et al.  Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time , 1997, IEEE Trans. Image Process..

[13]  Hassan Foroosh,et al.  Extension of phase correlation to subpixel registration , 2002, IEEE Trans. Image Process..

[14]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[15]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[16]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[17]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[18]  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.

[19]  B. Marcel,et al.  3 - Calcul de translation et rotation par la transformation de Fourier , 1997 .

[20]  Andrew J. Patti,et al.  Super Resolution Video Reconstruction with Arbitrary Sampling Lattices and Non-zero Aperture Time , 1997 .

[21]  Shmuel Peleg,et al.  Robust super-resolution , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Martin Vetterli,et al.  Double resolution from a set of aliased images , 2004, IS&T/SPIE Electronic Imaging.

[23]  Matthias Schwab,et al.  Making scientific computations reproducible , 2000, Comput. Sci. Eng..

[24]  Michal Irani,et al.  Computing occluding and transparent motions , 1994, International Journal of Computer Vision.

[25]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[26]  Subhasis Chaudhuri,et al.  Super-resolution imaging: use of zoom as a cue , 2004, Image Vis. Comput..

[27]  Li Meng,et al.  Subpixel Motion Estimation for Super-Resolution Image Sequence Enhancement , 1998, J. Vis. Commun. Image Represent..

[28]  Robert L. Stevenson,et al.  Spatial Resolution Enhancement of Low-Resolution Image Sequences A Comprehensive Review with Directions for Future Research , 1998 .

[29]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.