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31 August 2009 Hyperspectral image reconstruction based on an improved genetic algorithm
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lang Wang, Shuxu Guo, and Ruizhi Ren "Hyperspectral image reconstruction based on an improved genetic algorithm", Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550K (31 August 2009);

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