Abstract
In this chapter the problem of reconstructing a high resolution image from multiple aliased and shifted by sub-pixel shifts low resolution images is considered. The low resolution images are possibly degraded by unknown blurs and their sub-pixel shifts are not known. This problem is described in the frequency and spatial domains. Algorithms for providing solutions to it are reviewed. In addition, two approaches are presented in detail for solving this low-to-high resolution problem. In the first of these two approaches registration and restoration is performed simultaneously using the expectation-maximization (EM) algorithm. The high resolution image is then reconstructed using regularized interpolation which is performed as a separate step. For this reason this approach is abbreviated as RR-I which corresponds to registration/restorationinterpolation. In the second of these approaches registration, restoration and interpolation are perfomed simultaneously using the EM algorithm. Therefore this approach is abbreviated as RRI which corresponds to registration/restoration/interpolation. Numerical experiments are presented that demonstrate the effectiveness of the two approaches.
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References
K. Aizawa, T. Komatsu, and T. Saito, “A Scheme for Acquiring Very High Resolution Images Using Multiple Cameras,” IEEE Proc. ICASSP-92, San Francisco, CA, vol. III, pp. 289–292, 1992.
K. Aizawa, T. Komatsu, T. Saito, and M. Hatori, “Subpixel Registration for a High Resolution Imaging System Using Multiple Imagers,” IEEE Proc. ICASSP-93, Minneapolis, MN, vol. V, pp. 133–136, 1993.
P. E. Anuta, “Spatial Registration of Multispectral and Multitemporal Digital Imagery Using Fast Fourier Transform Techniques,” IEEE Trans. Geoscience Electronics, vol. GE-8, no. 4, pp. 353–368, Oct. 1970.
M. R. Banham and A. K. Katsaggelos, “Digital Image Restoration,” IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24–41, March 1997.
D. I. Barnea and H. F. Silverman, “A Class of Algorithms for Fast Digital Image Registration,” IEEE Trans. Computers, vol. C-21, no. 2, pp. 179–186, 1972.
N. K. Bose, H. C. Kim, H. M. Valenzuela, “Recursive Implementation of Total Least Squares Algorithm for Image Reconstruction from Noisy, Undersampled Multiframes,” IEEE Proc. ICASSP-93, Minneapolis, MN, vol. V, pp. 269–272, April 1993.
C. Cenker, H. G. Feichtinger and H. Steir, “Fast Iterative and Non-Iterative Reconstruction of Band-Limited Functions from Irregular Sampling Values,” IEEE Proc. ICASSP-91, Toronto, pp. 1773–1776, 1991.
A. D. Dempster, N. M. Laird and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM algorithm,” J. Roy. Stat. Soc., vol. B39, pp. 1–37, 1977.
F. DeNatale, G. Desoli, D. Giusto, and G. Vernazza, “A Spline-Like Scheme for Least-Squares Bilinear Interpolation of Images,” IEEE Proc. ICASSP-93, Minneapolis, MN, vol. V, pp. 141–144, 1993.
D. Dudgeon, and R. Mersereau, Multidimensional Digital Signal Processing, Prentice Hall 1984.
M. Elad and A. Feuer, “Restoration of a Single Superresolution Image from Several Blurred, Noisy, and Undersampled Measured Images,” IEEE Trans. on Image Processing, vol. 6, no. 12, pp. 1647–1657, December 1997.
H. G. Feichtinger and K. Gröchenig, “Iterative Reconstruction of Multivariate Band-Limited Functions from Irregular Sampling Values,” SIAM J. Math. Anal., vol. 23, no. 1, pp. 244–261, Jan. 1992.
B. R. Frieden and H. H. G. Aumann, “Image Reconstruction from Multiple 1-D Scans Using Filtered Localized Projections,” Appl. Opt., vol. 26, pp. 3615–3621, 1987.
N. P. Galatsanos and R. T. Chin, “Digital Restoration of Multi-channel Images,” IEEE Trans. Acoust., Speech, Signal Proc., vol. 37, no. 3, pp. 415–421, March 1989.
N.P. Galatsanos, A.K. Katsaggelos, R.T. Chin, A. Hillery, “Least Squares Restoration of Multi-Channel Images,” IEEE Trans. Signal Processing, vol. 39, no. 10, pp. 2222–2236, Oct. 1991.
N.P. Galatsanos and A.K. Katsaggelos, “Methods for Choosing the Regularization Parameter and Estimating the Noise Variance in Image Restoration and their Relation,” IEEE Trans. Image Processing, vol. 1, pp. 322–336, July 1992.
N. P. Galatsanos, M. Wernick, and A. K. Katsaggelos, “Multi-channel Image Recovery”, in Handbook of Image and Video Processing, A. Bovik, editor, ch. 3.7, pp. 161–174, Academic Press, 2000.
R. M. Gray, “On the Asymptotic Eigenevalue Distribution of Toeplitz Matrices”, IEEE Trans. on Information Theory, vol. IT-18, pp. 725–730, November 1972.
R. C. Hardie, K. J. Barnard, and E. E. Armstrong, “Joint MAP Registration and High-Resolution Image Estimation Using a Sequence of Undersampled Images”, IEEE Trans. on Image Processing, vol. 6, no. 12, pp. 1621–1633, December 1997.
M. Irani and S. Peleg, “Improving Resolution by Image Registration,” CVGIP: Graphical Models and Image Proc., vol. 53, pp. 231–239, May 1991.
A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1988.
M. G. Kang and A. K. Katsaggelos, “General Choice of the Regularization Functional in Regularized Image Restoration,” IEEE Trans. Image Proc., vol. 4, no. 5, pp. 594–602, May 1995.
A. K. Katsaggelos, “Iterative Image Restoration Algorithms,” Optical Engineering, vol. 28, no. 7, pp. 735–748, July 1989.
A. K. Katsaggelos, ed., Digital Image Restoration, New York: Springer-Verlag, 1991.
A. K. Katsaggelos and K. T. Lay, “Identification and Restoration of Images Using the Expectation Maximization Algorithm,” in Digital Image Restoration, A.K. Katsaggelos, editor, Springer-Verlag, 1991.
A. K. Katsaggelos, J. Biemond, R. M. Mersereau, and R. W. Schafer, “A Regularized Iterative Image Restoration Algorithm,” IEEE Trans. Signal Processing, vol. 39, no. 4, pp. 914–929, April 1991.
A. K. Katsaggelos, K. T. Lay, and N. P. Galatsanos, “A General Framework for Frequency Domain Multi-Channel Signal Processing,” IEEE Trans. Image Proc., vol. 2, no. 3, pp. 417–420, July 1993.
S. P. Kim and N. K. Bose, “Reconstruction of 2-D Bandlimited Discrete Signals from Nonuniform Samples,” IEE Proc., vol. 137, pt. F, no. 3, pp. 197–204, June 1990.
S. P. Kim, N. K. Bose and H. M. Valenzuela, “Recursive Reconstruction of High Resolution Image From Noisy Undersampled Multiframes,” IEEE Trans. Acoust., Speech, Signal Proc., vol. 38, no. 6, pp. 1013–1027, June 1990.
S. P. Kim, W. Y. Su, “Recursive High-Resolution Reconstruction of Blurred Multiframe Images,” IEEE Proc. ICASSP-91, Toronto, pp. 2977–2980, 1991.
S. P. Kim, W. Su, “Subpixel Accuracy Image Registration By Spectrum Cancellation,” IEEE Proc. ICASSP-93, Minneapolis, MN, vol. V, pp. 153–156, 1993.
K. T. Lay and A. K. Katsaggelos, “Image Identification and Restoration Based on the Expectation-Maximization Algorithm” Optical Engineering, vol. 29, pp. 436–445, May 1990.
M. S. Mort and M. D. Srinath, “Maximum Likelihood Image Registration With Subpixel Accuracy,” Proc. SPIE, vol. 974, pp. 38–44, 1988.
M. Nikolova, J. Idier, and A. Mohammad-Djafari, “Inversion of Large-support Ill-posed Linear Operators Using a Piecewise Gaussian MRF,” IEEE Transactions on Image Processing, vol. 7, no. 4, pp. 571–585, 1998.
A. Patti, M. Sezan and A. Tekalp, “Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Non-zero Apperture Time,”, IEEE Trans. Image Processing, vol. 6, pp. 1064–1076, August 1997.
W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd. ed., Cambridge University Press, 1992.
R. R. Schultz, R. L. Stevenson, “Extraction of High-Resolution Stills from Video Sequences”, IEEE Trans. in Image Processing, vol. 5, no. 6, pp. 996–1011, June 1996.
C. Srinivas and M. D. Srinath, “A Stochastic Model-Based Approach for Simultaneous Restoration of Multiple Misregistered Images,” Proc. SPIE, vol. 1360, pp. 1416–1427, 1990.
K. D. Sauer and J. P. Allebach, “Iterative Reconstruction of Band-Limited Images from Nonuniformly Spaced Samples,” IEEE Trans. Circuits and Systems, vol. 34, no. 10, pp. 1497–1505, Oct. 1987.
H. Stark and P. Oskoui, “High-resolution Image Recovery from Image-Plane Arrays, Using Convex Projections,” J. Opt. Soc. Amer. A, vol. 6, no. 11, pp. 1715–1726, Nov. 1989.
A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, “High-Resolution Image Reconstruction from Lower-Resolution Image Sequences and Space-Varying Image Restoration,” IEEE Proc. ICASSP 92, San Francisco, vol. III, pp. 169–172, 1992.
A. Tikhonov and V. Arsenin, Solution of Ill-Posed Problems, John Wiley and Sons, 1977.
B. C. Tom and A. K. Katsaggelos, “Reconstruction of a High Resolution Image from Multiple Degraded Mis-Registered Low Resolution Images,” Proc. SPIE, Visual Communications and Image Processing, Chicago, IL, vol. 2308, pt. 2, pp. 971–981, Sept. 1994.
B. C. Tom, A. K. Katsaggelos, and N. P. Galatsanos, “Reconstruction of a High Resolution from Registration and Restoration of Low Resolution Images,” IEEE Proc. International Conference on Image Processing, Austin, TX, vol. 3, pp. 553–557, Nov. 1994.
B. C. Tom and A. K. Katsaggelos, “Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images,” IEEE Proc. International Conference on Image Processing, Washington D.C., vol. 2, pp. 539–542, Oct. 1995.
B. C. Tom, “Reconstruction of a High Resolution Image from Multiple Degraded Mis-registered Low Resolution Images,” Ph.D. Thesis, Northwestern University, Department of ECE, December 1995.
B. C. Tom, K. T. Lay and A. K. Katsaggelos, “Multi-Channel Image Identification and Restoration Using the Expectation-Maximization Algorithm,” Optical Engineering, “Special Issue on Visual Communications and Image Processing”, vol. 35, no. 1, pp. 241–254 Jan. 1996.
B. C. Tom and A. K. Katsaggelos, “Resolution Enhancement of Monochrome and Color Video Using Motion Compensation,” Trans Image Proc., vol. 10, no. 2, pp. 278–287, Feb. 2001.
R. Y. Tsai and T. S. Huang, “Multiframe Image Restoration and Registration,” Advances in Computer Vision and Image Processing, vol. 1, T. S. Huang, ed., Greenich,CT: Jai Press, ch. 7, pp. 317–339, 1984.
S. Yeh and H. Stark, “Iterative and One-Step Reconstruction from Nonuniform Samples by Convex Projections,” J. Opt. Soc. Amer. A, vol. 7, no. 3, pp. 491–499, 1990.
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Tom, B.C., Galatsanos, N.P., Katsaggelos, A.K. (2002). Reconstruction of a High Resolution Image from Multiple Low Resolution Images. In: Chaudhuri, S. (eds) Super-Resolution Imaging. The International Series in Engineering and Computer Science, vol 632. Springer, Boston, MA. https://doi.org/10.1007/0-306-47004-7_4
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DOI: https://doi.org/10.1007/0-306-47004-7_4
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