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Proceedings Paper

Tree structure matching pursuit based on Gaussian scale mixtures model
Author(s): Peng Liu; Zhiwen Liu; Jingbo Wei; Dingsheng Liu
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Paper Abstract

Compressed Sensing (CS) theory has gained so much attention recently in the areas of signal processing. The sparsity of the transform coefficients has been widely employed in the early CS recovery techniques. However, except for the sparsity, there are other priors about transform coefficients such as the tree structure and the statistical dependencies that could be employed in CS reconstruction. In this paper, we propose to introduce the Gaussian Scale Mixtures (GSM) model into the tree structure based Orthogonal Matching Pursuit (TSOMP) reconstruction algorithm. This GSM model can efficiently denote the statistical dependencies between wavelet coefficients. And these statistical dependencies will improve the accuracy of the searching of the tree structure subspace in TSOMP algorithm. When both the inter-scale dependences (such as GSM model) of the coefficients and the intra-scale dependences (such as tree structure) of the coefficients are combined into the Orthogonal Matching Pursuit reconstruction algorithm, the noise and instability in TSOMP reconstruction are well reduced. Some state-of-the-art methods are compared with the proposed method. Experimental results show that the proposed method improves reconstruction accuracy for a given number of measurements and more image details are recovered.

Paper Details

Date Published: 24 September 2011
PDF: 8 pages
Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 813520 (24 September 2011); doi: 10.1117/12.903684
Show Author Affiliations
Peng Liu, Ctr. for Earth Observation and Digital Earth (China)
Zhiwen Liu, Ctr. for Earth Observation and Digital Earth (China)
Jingbo Wei, Ctr. for Earth Observation and Digital Earth (China)
Dingsheng Liu, Ctr. for Earth Observation and Digital Earth (China)


Published in SPIE Proceedings Vol. 8135:
Applications of Digital Image Processing XXXIV
Andrew G. Tescher, Editor(s)

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