Continuous Super-Resolution for Recovery of 1-D Image Features: Algorithm and Performance Modeling
Abstract
We consider the recovery of 1-D image features. Such features can be described by a noisy, blurred and undersampled image of a unique 1-D profile. The profiles recovery is modeled as a 1-D continuous super-resolution (SR) problem. We adopt a functional estimation within a Tikhonov regularization framework. A linear closed-form solution is derived and applied to real data for bar code recovery from low-resolution video frames. Performance modeling in then considered. Thanks to a continuous stochastic model of the input profile, we define a quantitative performance measure which is a mean-square error averaged over a class of profiles with tunable regularity. As a result, an expected SR resolution enhancement ratio is computed, which depends on experimental parameters: SNR, number of input images, sampling rate. A good agreement is found between this theoretical study and empirical performance in experimental SR recovery of bar code profiles.
Cite
Text
Champagnat et al. "Continuous Super-Resolution for Recovery of 1-D Image Features: Algorithm and Performance Modeling." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.87Markdown
[Champagnat et al. "Continuous Super-Resolution for Recovery of 1-D Image Features: Algorithm and Performance Modeling." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/champagnat2006cvpr-continuous/) doi:10.1109/CVPR.2006.87BibTeX
@inproceedings{champagnat2006cvpr-continuous,
title = {{Continuous Super-Resolution for Recovery of 1-D Image Features: Algorithm and Performance Modeling}},
author = {Champagnat, Frédéric and Le Besnerais, Guy and Kulcsar, Caroline},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {916-926},
doi = {10.1109/CVPR.2006.87},
url = {https://mlanthology.org/cvpr/2006/champagnat2006cvpr-continuous/}
}