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- W2914779134 abstract "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 a combination 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— Belief propagation, Graphical model, Regression, Super resolution." @default.
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- W2914779134 date "2007-02-24" @default.
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- W2914779134 title "Multiple Regression based Graphical Modeling for Images" @default.
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