%0 Journal Article
	%A Pavan S. and  Sridhar G. and  Sridhar V.
	%D 2007
	%J International Journal of Aerospace and Mechanical Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 2, 2007
	%T Multiple Regression based Graphical Modeling for Images 
	%U https://publications.waset.org/pdf/2586
	%V 2
	%X 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.

	%P 469 - 472