Super-Resolution Reconstruction of Image Sequences
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
Super-Resolution Imaging
Digital Image Restoration
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Super-resolution of images based on local correlations
IEEE Transactions on Neural Networks
Hallucinating multiple occluded face images of different resolutions
Pattern Recognition Letters
Hallucinating multiple occluded face images of different resolutions
Pattern Recognition Letters - Special issue on vision for crime detection and prevention
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Adaptive Markov random fields for example-based super-resolution of faces
EURASIP Journal on Applied Signal Processing
Learning-based nonparametric image super-resolution
EURASIP Journal on Applied Signal Processing
Limits of Learning-Based Superresolution Algorithms
International Journal of Computer Vision
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Visio-lization: generating novel facial images
ACM SIGGRAPH 2009 papers
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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In this paper, we consider the problem of super-resolving a human face video by a very high (×16) zoom factor. Inspired by recent literature on hallucination and examplebased learning, we formulate this task using a graphical model that encodes 1) spatio-temporal consistencies, and 2) image formation & degradation processes. A video database of facial expressions is used to learn a domainspecific prior for high-resolution videos. The problem is posed as one of probabilistic inference, in which we aim to find the high resolution video that best satisfies the constraints expressed through the graphical model. Traditional approaches to this problem using video data first estimate the relative motion between frames and then compensate for it, effectively resulting in multiple measurements of the scene. Our use of time is rather direct: We define data structures that span multiple consecutive frames, enriching our feature vectors with a temporal signature. We then exploit these signatures to find consistent solutions over time. In our experiments, a 8 × 6 pixel-wide face video, subject to translational jitter and additive noise, gets magnified to a 128 × 96 pixel video. Our results show that by exploiting both space and time, drastic improvements can be achieved in both video flicker artifacts and mean-squared-error.