Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
Motion Deblurring and Super-resolution from an Image Sequence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Local Blur Estimation and Super-Resolution
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Restoration and Recognition in a Loop
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
High-zoom video hallucination by exploiting spatio-temporal regularities
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Models for patch-based image restoration
Journal on Image and Video Processing - Special issue on patches in vision
License plate localization based on a probabilistic model
Machine Vision and Applications
Nonparametric belief propagation
Communications of the ACM
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
A discontinuity adaptive method for super-resolution of license plates
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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We present a novel learning-based framework for zooming and recognizing images of digits obtained from vehicle registration plates, which have been blurred using an unknown kernel. We model the image as an undirected graphical model over image patches in which the compatibility functions are represented as nonparametric kernel densities. The crucial feature of this work is an iterative loop that alternates between super-resolution and restoration stages. A machine-learning-based framework has been used for restoration which also models spatial zooming. Image segmentation is done by a column-variance estimation-based "dissection" algorithm. Initially, the compatibility functions are learned by nonparametric kernel density estimation, using random samples from the training data. Next, we solve the inference problem by using an extended version of the nonparametric belief propagation algorithm, in which we introduce the notion of partial messages. Finally, we recognize the super-resolved and restored images. The resulting confidence scores are used to sample from the training set to better learn the compatibility functions.