Multiscale and Multitopic Sparse Representation for Multisensor Infrared Image Superresolution
Joint Authors
Yang, Xiaomin
Yan, Binyu
Liu, Kai
Gan, Zhongliang
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-02
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Methods based on sparse coding have been successfully used in single-image superresolution (SR) reconstruction.
However, the traditional sparse representation-based SR image reconstruction for infrared (IR) images usually suffers from three problems.
First, IR images always lack detailed information.
Second, a traditional sparse dictionary is learned from patches with a fixed size, which may not capture the exact information of the images and may ignore the fact that images naturally come at different scales in many cases.
Finally, traditional sparse dictionary learning methods aim at learning a universal and overcomplete dictionary.
However, many different local structural patterns exist.
One dictionary is inadequate in capturing all of the different structures.
We propose a novel IR image SR method to overcome these problems.
First, we combine the information from multisensors to improve the resolution of the IR image.
Then, we use multiscale patches to represent the image in a more efficient manner.
Finally, we partition the natural images into documents and group such documents to determine the inherent topics and to learn the sparse dictionary of each topic.
Extensive experiments validate that using the proposed method yields better results in terms of quantitation and visual perception than many state-of-the-art algorithms.
American Psychological Association (APA)
Yang, Xiaomin& Liu, Kai& Gan, Zhongliang& Yan, Binyu. 2015. Multiscale and Multitopic Sparse Representation for Multisensor Infrared Image Superresolution. Journal of Sensors،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1110576
Modern Language Association (MLA)
Yang, Xiaomin…[et al.]. Multiscale and Multitopic Sparse Representation for Multisensor Infrared Image Superresolution. Journal of Sensors No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1110576
American Medical Association (AMA)
Yang, Xiaomin& Liu, Kai& Gan, Zhongliang& Yan, Binyu. Multiscale and Multitopic Sparse Representation for Multisensor Infrared Image Superresolution. Journal of Sensors. 2015. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1110576
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110576