Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
A non-parametric multi-scale statistical model for natural images
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Frontal face generation from multiple low-resolution non-frontal faces for face recognition
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Eigentransformation-based face super-resolution in the wavelet domain
Pattern Recognition Letters
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Learning-based super-resolution has recently been proposed for enhancing human face images, known as ''face hallucination''. In this paper, we propose a novel algorithm to super-resolve face images given multiple partially occluded inputs at different lower resolutions. By integrating hierarchical patch-wise alignment and inter-frame constraints into a Bayesian framework, we can probabilistically align multiple input images at different resolutions and recursively infer the high-resolution face image. We address the problem of fusing partial imagery information through multiple frames and discuss the new algorithm's effectiveness when encountering occluded low-resolution face images. We show promising results compared to those of existing face hallucination methods from both simulated facial database and live video sequences.