Abstract
This paper addresses the problem of super resolution – obtaining a single high-resolution image given a set of low resolution images which are related by small displacements. We employ a reconstruction based approach using MRF-MAP formalism, and use approximate optimization using graph cuts to carry out the reconstruction. We also use the same formalism to investigate high resolution expansions from single images by deconvolution assuming that the point spread function is known. We present a method for the estimation of the point spread function for a given camera. Our results demonstrate that it is possible to obtain super-resolution preserving high frequency details well beyond the predicted limits of magnification.
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References
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on PAMI 23, 1222–1239 (2001)
Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP:Graphical Models and Image Processing 53, 231–239 (1991)
Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation 4, 324–335 (1993)
Borman, S., Stevenson, R.: Linear models for multi-frame super-resolution restoration under non-affine registration and spatially varying PSF. In: Bouman, C., Miller, E. (eds.) Computational Imaging II, Proceedings of the SPIE,San Jose, CA, USA, vol. 5299, pp. 234–245 (2004)
Rajan, D., Chaudhuri, S.: Simultaneous estimation of super-resolved scene and depth map for low resolution defocused observations. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 1102–1117 (2003)
Joshi, M., Chaudhuri, S.: A learning based method for image super-resolution from zoomed observations. In: Proc. fifth Int. Conf. on Advances in Pattern Recognition, pp. 179–182 (2003)
Chaudhuri, S., Joshi, M.V.: Motion-Free Super-Resolution. Springer, Heidelberg (2004)
Schultz, R.R., Stevenson, R.L.: A bayesian approach to image expansion for improved definition. IEEE transctions on Image processing 3, 233–242 (1994)
Capel, D., Zisserman, A.: Computer vision applied to super resolution. IEEE Signal Processing Magazine, 75–86 (2003)
Pickup, L.C., Roberts, S.J., Zisserman, A.: A sampled texture prior for image super-resolution. Advances in Neural Information Processing Systems (2003)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE computer Graphics and Applications, 56–65 (2002)
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. In: Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition (2000)
Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 83–97 (2004)
Borman, S., Stevenson, R.: Super-resolution from image sequences – A review. In: Proceedings of the 1998 Midwest Symposium on Circuits and Systems, Notre Dame, IN, USA, pp. 374–378 (1998)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on PAMI 26, 1124–1137 (2004)
Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. In: European Conference on Computer Vision (2002)
Elad, M., Feuer, A.: Super resolution restoration of an image sequence: Adaptive filtering approach. IEEE Trans. on Image Processing 8, 387–395 (1999)
Bergen, J.R., Anandan, P., Hanna, K.J., Hingorani, R.: Hierarchical model-based motion estimation. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 237–252. Springer, Heidelberg (1992)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on PAMI 26 (2004)
Kolmogorov, V.: http://research.microsoft.com/~vnk/ , (2000)
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Mudenagudi, U., Singla, R., Kalra, P., Banerjee, S. (2006). Super Resolution Using Graph-Cut. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_39
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DOI: https://doi.org/10.1007/11612704_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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