Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction
Joint Authors
Li, Chun-mei
Deng, Ka-zhong
Sun, Jiu-yun
Wang, Hui
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-03
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The spatial resolution of digital images is the critical factor that affects photogrammetry precision.
Single-frame, superresolution, image reconstruction is a typical underdetermined, inverse problem.
To solve this type of problem, a compressive, sensing, pseudodictionary-based, superresolution reconstruction method is proposed in this study.
The proposed method achieves pseudodictionary learning with an available low-resolution image and uses the K -SVD algorithm, which is based on the sparse characteristics of the digital image.
Then, the sparse representation coefficient of the low-resolution image is obtained by solving the norm of l 0 minimization problem, and the sparse coefficient and high-resolution pseudodictionary are used to reconstruct image tiles with high resolution.
Finally, single-frame-image superresolution reconstruction is achieved.
The proposed method is applied to photogrammetric images, and the experimental results indicate that the proposed method effectively increase image resolution, increase image information content, and achieve superresolution reconstruction.
The reconstructed results are better than those obtained from traditional interpolation methods in aspect of visual effects and quantitative indicators.
American Psychological Association (APA)
Li, Chun-mei& Deng, Ka-zhong& Sun, Jiu-yun& Wang, Hui. 2016. Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction. Journal of Sensors،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1110309
Modern Language Association (MLA)
Li, Chun-mei…[et al.]. Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction. Journal of Sensors No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1110309
American Medical Association (AMA)
Li, Chun-mei& Deng, Ka-zhong& Sun, Jiu-yun& Wang, Hui. Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1110309
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110309