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Multiple Regression based Graphical Modeling for Images

Authors: Pavan S., Sridhar G., Sridhar V.


Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.

Keywords: Super Resolution, Regression, belief propagation, Graphical model

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[1] William K.Pratt, Digital Image Processing, 3rd ed., John Wiley and sons, 2003, pp. 393-397.
[2] W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, “Learning lowlevel vision," in International Journal of Computer Vision, 40(1): 25- 47, 2000.
[3] Marshall F. Tappen, Bryan C. Russell, and W .T. Freeman, “Efficient graphical models for processing images", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
[4] Jonathan S. Yedidia, W.T.Freeman, and Yair Weiss, “Understanding belief propagation and its generalizations, Technical Report TR2001-22, MERL, 2001".
[5] R. Rosales, K. Achan, and B. Frey, “Unsupervised image translation," in Ninth International Conference on Computer Vision (ICCV), 2003.
[6] S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation," IEEE Trans. on Pattern Analysis and Machine Intelligence, 11(7):674-694, July 1989.
[7] Hong Chang, Dit-Yan Yeung, and Yimin Xiong,“Super-Resolution through neighbor embedding," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
[8] H. Greenspan, C. Anderson, and S. Akber, “Image enhancement by nonlinear extrapolation in frequency space," IEEE Trans. on Image Processing, 9(6), 2000.
[9] B. Morse, and D. Schwartzwald,"Image magnification using level set reconstruction," Proc. International Conf. Computer Vision (ICCV), pages 333-341, 2001.
[10] J.Pearl,"Probabilistic reasoning in intelligent systems: networks of plausible inference," Morgan Kaufmann, 1988.
[11] H. H. Hou, and H. C. Andrews, “Cubic splines for image interpolation and digital filtering," in IEEE Trans.Acoust.Speech Signal Processing, ASSP-26(6):508-517, 1978.
[12] D. Geiger, and F. Girosi, “Parallel and deterministic algorithms from MRF-s: Surface reconstruction," in IEEE Pattern Analysis and Machine Intelligence, 13(5), 401-412, 1991.
[13] F. R. Kschischang, B. J.Frey, and H. A. Loeliger,"Factor graphs and the sum-product algorithm," in IEEE Transactions on Information Theory, 42(2): 498-519, 2001.
[14] M. Belge, M. Kilmer, and E. Miller,"Wavelet domain image restoration with adaptive edge-preserving regularity," in IEEE Transactions on Image Processing, 9(4): 597-608, 2000.