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Proceedings Paper

Image compressed sensing based on dictionary learning via bilinear generalized approximate message passing
Author(s): Jingjing Si; Jiaoyun Wang; Yinbo Cheng
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Paper Abstract

Sparse representation matrix is of great significance for compressed sensing (CS). When dictionaries learned from training data are used instead of predefined dictionaries, signal reconstruction accuracy would be improved. In this paper, we learn dictionaries for compressed image reconstruction based on bilinear generalized approximate message passing (BiGAMP). Stochastic mapping is performed on the training data which are composed of image blocks, to conform to the statistical model of BiGAMP methodology. Square dictionary and overcomplete dictionary are learned respectively for blocked image sparse representation, and are applied to image CS reconstruction. Simulation results show that our learned dictionaries lead to improved image CS reconstruction performance in comparison to predefined dictionaries and dictionaries learned with K-SVD method.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064C (9 August 2018); doi: 10.1117/12.2502839
Show Author Affiliations
Jingjing Si, Yanshan Univ. (China)
Hebei Key Lab. of Information Transmission and Signal Processing (China)
Jiaoyun Wang, Yanshan Univ. (China)
Hebei Key Lab. of Information Transmission and Signal Processing (China)
Yinbo Cheng, Ocean College of Hebei Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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