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Proceedings Paper

Reconstruction of images from compressive sensing based on the stagewise fast LASSO
Author(s): Jiao Wu; Fang Liu; Licheng Jiao
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Paper Abstract

Compressive sensing (CS) is a theory about that one may achieve a nearly exact signal reconstruction from the fewer samples, if the signal is sparse or compressible under some basis. The reconstruction of signal can be obtained by solving a convex program, which is equivalent to a LASSO problem with l1-formulation. In this paper, we propose a stage-wise fast LASSO (StF-LASSO) algorithm for the image reconstruction from CS. It uses an insensitive Huber loss function to the objective function of LASSO, and iteratively builds the decision function and updates the parameters by introducing a stagewise fast learning strategy. Simulation studies in the CS reconstruction of the natural images and SAR images widely applied in practice demonstrate that the good reconstruction performance both in evaluation indexes and visual effect can be achieved by StF-LASSO with the fast recovered speed among the algorithms which have been implemented in our simulations in most of the cases. Theoretical analysis and experiments show that StF-LASSO is a CS reconstruction algorithm with the low complexity and stability.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749848 (30 October 2009); doi: 10.1117/12.832470
Show Author Affiliations
Jiao Wu, Xidian Univ. (China)
Ministry of Education of China (China)
China Jiliang Univ. (China)
Fang Liu, Xidian Univ. (China)
Ministry of Education of China (China)
Licheng Jiao, Ministry of Education of China (China)
Xidian Univ. (China)

Published in SPIE Proceedings Vol. 7498:
MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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