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Proceedings Paper

Image super-resolution based on compressive sensing
Author(s): Ying Gu; Xiuchang Zhu
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Paper Abstract

Based on Compressive Sensing, we introduce sparse signal representation theory to modify the local geometric similarity model and construct sparse geometric similarity representation. Based on the modified model we can estimate the optimized reconstruct coefficients by jointing the original global and local image structure themselves, without the support of other training image database. The experimental results show that the algorithm can greatly improve the reconstruction of the edge and texture details in the high-resolution image.

Paper Details

Date Published: 1 October 2011
PDF: 7 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828575 (1 October 2011); doi: 10.1117/12.913513
Show Author Affiliations
Ying Gu, Nanjing Univ. of Posts and Telecommunications (China)
Xiuchang Zhu, Nanjing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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