Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamentals of digital image processing
Fundamentals of digital image processing
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
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
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Example-based image super-resolution with class-specific predictors
Journal of Visual Communication and Image Representation
Models for patch-based image restoration
Journal on Image and Video Processing - Special issue on patches in vision
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
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Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.