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Proceedings Paper

Hyperspectral image reconstruction based on an improved genetic algorithm
Author(s): Lang Wang; Shuxu Guo; Ruizhi Ren
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Paper Abstract

A novel theory of information acquisition-"compressive sampling" has been applied in this paper, and goes against the common wisdom in data acquisition of Shannon theorem. CS theory asserts that one can recover certain signals and images perfectly from far fewer samples or measurements than traditional methods use. This paper presents an improvement on genetic algorithm instead of match pursuit algorithm in consideration of the enormous computational complexity on sparse decomposition. Then the whole image is divided into small blocks which can be processed by sparse decomposition, and an end to decomposition is determined by PSNR threshold adaptively. At last, the experiment results show that good performance on image reconstruction with less computational complexity has been achieved.

Paper Details

Date Published: 31 August 2009
PDF: 8 pages
Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550K (31 August 2009); doi: 10.1117/12.825603
Show Author Affiliations
Lang Wang, Jilin Univ. (China)
Shuxu Guo, Jilin Univ. (China)
Ruizhi Ren, Jilin Univ. (China)


Published in SPIE Proceedings Vol. 7455:
Satellite Data Compression, Communication, and Processing V
Bormin Huang; Antonio J. Plaza; Raffaele Vitulli, Editor(s)

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