Increasing Space-Time Resolution in Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Feature Based Methods for Structure and Motion Estimation
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
A super-resolution method with EWA
Journal of Computer Science and Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Super-Resolution Using Controlled Subpixel Detector Shifts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low-cost super-resolution algorithms implementation over a HW/SW video compression platform
EURASIP Journal on Applied Signal Processing
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Mosaicing-by-recognition for video-based text recognition
Pattern Recognition
Robust registration of manuscript images
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Adaptive large scale artifact reduction in edge-based image super-resolution
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Regularized super-resolution of brain MRI
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Jitter camera: high resolution video from a low resolution detector
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Local geometry driven image magnification and applications to super-resolution
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
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The objective of this work is the super-resolution enhancement of image sequences. We consider in particular images of scenes for which the point-to-point image transformation is a plane projective transformation. We first describe the imaging model, and a maximum likelihood (ML) estimator of the super-resolution image. We demonstrate the extreme noise sensitivity of the unconstrained ML estimator. We show that the Irani and Peleg [9, 10] super-resolution algorithm does not suffer from this sensitivity, and explain that this stability is due to the error back-projection method, which effectively constrains the solution. We then propose two estimators suitable for the enhancement of text images: a maximum a posterior (MAP) estimator based on a Huber prior, and an estimator regularized using the Total Variation norm. We demonstrate the improved noise robustness of these approaches over the Irani and Peleg estimator. We also show the effects of a poorly estimated point spread function (PSF) on the super-resolution result and explain conditions necessary for this parameter to be included in the optimization. Results are evaluated on both real and synthetic sequences of text images. In the case of the real images, the projective transformations relating the images are estimated automatically from the image data, so that the entire algorithm is automatic.