Paper Info
Title
Efficient graphical models for processing images
Abstract
Graphical models are powerful tools for processing images. However, the large dimensionality of even local image data poses a difficulty: representing the range of possible graphical model node variables with discrete states leads to an overwhelmingly large number of states for the model, often making both exact and approximate inference computationally intractable. We propose a representation that allows a small number of discrete states to represent the large number of possible image values at each pixel or local image patch. Each node in the graph represents the best regression function, chosen from a set of candidate functions, for estimating the unobserved image pixels from the observed samples. This permits a small number of discrete states to summarize the range of possible image values at each point in the image. Belief propagation is then used to find the best regressor to use at each point. To demonstrate the usefulness of this technique, we apply it to two problems: super-resolution and color demosaicing. In both cases, we find our method compares well against other techniques for these problems.
Year
DOI
Venue
2004
10.1109/CVPR.2004.1315229
CVPR
Keywords
Field
DocType
graph theory,image colour analysis,image representation,image resolution,image segmentation,regression analysis,color demosaicing,discrete states,graphical models,image processing,inference computationally intractable,regression function,super resolution image
Computer vision,Pattern recognition,Feature detection (computer vision),Computer science,Image texture,Binary image,Approximate inference,Image segmentation,Artificial intelligence,Graphical model,Image histogram,Belief propagation
Conference
Volume
ISSN
Citations 
2
1063-6919
26
PageRank 
References 
Authors
3.07
12
3
Name
Order
Citations
PageRank
Marshall F. Tappen1190189.34
Bryan C. Russell22570217.78
William T. Freeman3173821968.76