@inproceedings{ec459d278cfa46a19f6936f0e4178a1f,
title = "Outlier removal power of the L1-norm super-resolution",
abstract = "Super-resolution combines several low resolution images having different sampling into a high resolution image. L1-norm data fit minimization has been proposed to solve this problem in a robust way. The outlier rejection capability of this methods has been shown experimentally for super-resolution. However, existing approaches add a regularization term to perform the minimization while it may not be necessary. In this paper, we recall the link between robustness to outliers and the sparse recovery framework. We use a slightly weaker Null Space Property to characterize this capability. Then, we apply these results to super resolution and show both theoretically and experimentally that we can quantify the robustness to outliers with respect to the number of images.",
keywords = "L1-norm, interpolation, super-resolution",
author = "Yann Traonmilin and Sa{\"i}d Ladjal and Andr{\'e}s Almansa",
year = "2013",
month = sep,
day = "25",
doi = "10.1007/978-3-642-38267-3\_17",
language = "English",
isbn = "9783642382666",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "198--209",
booktitle = "Scale Space and Variational Methods in Computer Vision - 4th International Conference, SSVM 2013, Proceedings",
note = "4th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2013 ; Conference date: 02-06-2013 Through 06-06-2013",
}