@inproceedings{3987655f11ee445a99bb356150382ca4,
title = "Single image super-resolution using dictionary-based local regression",
abstract = "This paper presents a new method of producing a high-resolution image from a single low-resolution image without any external training image sets. We use a dictionary-based regression model for practical image super-resolution using local self-similar example patches within the image. Our method is inspired by the observation that image patches can be well represented as a sparse linear combination of elements from a chosen over-complete dictionary and that a patch in the high-resolution image have good matches around its corresponding location in the low-resolution image. A first-order approximation of a nonlinear mapping function, learned using the local self-similar example patches, is applied to the low-resolution image patches to obtain the corresponding high-resolution image patches. We show that the proposed algorithm provides improved accuracy compared to the existing single image super-resolution methods by running them on various input images that contain diverse textures, and that are contaminated by noise or other artifacts.",
keywords = "Image restoration, dictionary learning, image super-resolution, regression, sparse recovery",
author = "Sundaresh Ram and Rodr{\'i}guez, {Jeffrey J.}",
year = "2014",
doi = "10.1109/SSIAI.2014.6806044",
language = "English (US)",
isbn = "9781479940530",
series = "Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "121--124",
booktitle = "2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - Proceedings",
note = "2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 ; Conference date: 06-04-2014 Through 08-04-2014",
}