Abstract
Superresolution is a term used to describe the generation of high-resolution images from a sequence of low-resolution images. In this paper an algorithm proposed in 2010, which gets superresolution images through Bayeasian approximate inference using a Markov chain Monte Carlo (MCMC) method, is revised. From the original equations, a closed form to calculate the high resolution image is derived, and a new algorithm is thus proposed. Several simulations, from which two results are here presented, show that the proposed algorithm performs better, in comparison with other superresolution algorithms.
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References
Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration. In: Tsai, R.Y., Huang, T.S. (eds.) Advances in Computer Vision and Image Processing, vol. 1, pp. 317–339. JAI Press Inc., Greenwich (1984)
Tekalp, A.M., Ozkan, M.K., Sezan, M.I.: High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration. In: ICASSP, San Francisco, vol. III, pp. 169–172 (1992)
Kim, S.P., Bose, N.K., Valenzuela, H.M.: Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Trans. ASSP 38(6), 1013–1027 (1990)
Kim, S.P., Su, W.-Y.: Recursive high-resolution reconstruction of blurred multiframe images. IEEE Trans. IP 2, 534–539 (1993)
Bose, N.K., Kim, H.C., Valenzuela, H.M.: Recursive Total Least Squares Algorithm for Image Reconstruction from Noisy, Undersampled Multiframes. Multidimensional Systems and Signal Processing 4(3), 253–268 (1993)
Borman, S., Stevenson, R.L.: Super-Resolution from Image Sequences - A Review. In: Midwest Symposium on Circuits and Systems (1998)
Komatsu, T., Igarashi, T., Aizawa, K., Saito, T.: Very high resolution imaging scheme with multiple different aperture cameras. Signal Processing Image Communication 5, 511–526 (1993)
Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion and transparency. Journal of Visual Communications and Image Representation 4, 324–335 (1993)
Patti, A.J., Sezan, M.I., Tekalp, A.M.: Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time. IEEE Trans. IP 6(8), 1064–1076 (1997)
Tom, B.C., Katsaggelos, A.K.: An Iterative Algorithm for Improving the Resolution of Video Sequences. In: SPIE VCIP, Orlando, vol. 2727, pp. 1430–1438 ( March 1996)
Eren, P.E., Sezan, M.I., Tekalp, A.: Robust, Object-Based High-Resolution Image Reconstruction from Low-Resolution Video. IEEE Trans. IP 6(10), 1446–1451 (1997)
Hong, M.-C., Kang, M.G., Katsaggelos, A.K.: A regularized multichannel restoration approach for globally optimal high resolution video sequence. In: SPIE VCIP, San Jose, vol. 3024, pp. 1306–1316 (February 1997)
Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. IP 5(6), 996–1011 (1996)
Cheeseman, P., Kanefsky, B., Kraft, R., Stutz, J., Hanson, R.: Super-resolved surface reconstruction from multiple images. In: Maximum Entropy and Bayesian Methods, pp. 293–308. Kluwer, Santa Barbara (1996)
Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Joint MAP Registration and High-Resolution Image Estimation Using a Sequence of Undersampled Images. IEEE Trans. IP 6(12), 1621–1633 (1997)
Tom, B.C., Katsaggelos, A.K.: Reconstruction of a high resolution image from multiple degraded mis-registered low resolution images. In: SPIE VCIP, Chicago, vol. 2308, pp. 971–981 (September 1994)
Tipping, M.E., Bishop, C.M.: Bayesian image super-resolution. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Nueral Information Processing Systems, vol. 15. MIT Press, Cambridge (2003)
Tian a, J., Ma, K.-K.: Stochastic super-resolution image reconstruction. J. Vis. Commun. Image R, R 21, 232–244 (2010)
Vandewalle, P., Susstrunk, S., Vetterli, M.: A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP Journal on Applied Signal Processing (2006)
Pham, T.Q., van Vliet, L.J., Schutte, K.: Robust fusion of irregularly sampled data using adaptive normalized convolution. EURASIP Journal on Applied Signal Processing (2006)
Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, New York (1995)
Rue, H.: Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall, Boca Raton (2005)
Galatsanos, N.P., Mesarovic, V.Z., Molina, R., Katsaggelos, A.K.: Hierarchical Bayesian image restoration from partially known blurs. IEEE Transactions on Image Processing 9, 1784–1797 (2000)
Figueiredo, M., Nowak, R.: Wavelet-based image estimation: an empirical Bayes approach using Jeffreys’ noninformative prior. IEEE Transactions on Image Processing 10, 1322–1331 (2001)
Bishop Christopher, M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
He, Y., Yap, K.-H., Chen, L., Chau, L.-P.: A soft MAP framework for blind superresolution image reconstruction. Image and Vision Computing 27, 364–373 (2009)
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Camponez, M.O., Salles, E.O.T., Sarcinelli-Filho, M. (2011). A Closed Form Algorithm for Superresolution. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_34
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DOI: https://doi.org/10.1007/978-3-642-24031-7_34
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