Efficient graphical models for processing images

Authors:
Marshall F. Tappen;Bryan C. Russell;William T. Freeman
Affiliations:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA
Venue:
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Year:
2004

Citing 11
Cited 9

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Low-Level Vision

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Super-Resolution

IEEE Computer Graphics and Applications
Assorted Pixels: Multi-sampled Imaging with Structural Models

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Hallucinating Faces

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Unsupervised Image Translation

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory
Wavelet domain image restoration with adaptive edge-preserving regularization

IEEE Transactions on Image Processing
Image enhancement by nonlinear extrapolation in frequency space

IEEE Transactions on Image Processing

Projection defocus analysis for scene capture and image display

ACM SIGGRAPH 2006 Papers
Image upsampling via imposed edge statistics

ACM SIGGRAPH 2007 papers
Face Hallucination: Theory and Practice

International Journal of Computer Vision
Fast image/video upsampling

ACM SIGGRAPH Asia 2008 papers
Labeling irregular graphs with belief propagation

IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Low-resolution gait recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Image and video upscaling from local self-examples

ACM Transactions on Graphics (TOG)
Efficient belief propagation with learned higher-order markov random fields

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Super-resolution reconstruction of faces by enhanced global models of shape and texture

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

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.