A Fast Recovery Method of 2D Geometric Compressed Sensing Signal

This paper presents a compression method based on compressed sensing for 2D contour models. And low rank random matrixes are used to sample 2D contour models, since the models can be sparsely represented under their Laplace operators. In the recovery process, a new function is designed as the optima...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2015-05, Vol.34 (5), p.1711-1724
Main Authors: Du, Zhuo-Ming, Ye, Fei-Yue, Shi, Hang, Zhu, Guang-Ping
Format: Article
Language:English
Subjects:
Circuits and Systems
Compressed
Detection
Electrical Engineering
Electronics and Microelectronics
Engineering
Geometry
Instrumentation
Linear equations
Mathematical models
Matrix
Optimization
Recovery
Shape
Short Paper
Signal processing
Signal,Image and Speech Processing
Two dimensional
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Summary:This paper presents a compression method based on compressed sensing for 2D contour models. And low rank random matrixes are used to sample 2D contour models, since the models can be sparsely represented under their Laplace operators. In the recovery process, a new function is designed as the optimal objective function to replace signal’s 1-norm, while a new search direction is constructed to find the solution, and to ensure that the solution speed is equal to the speed of the linear optimization. Finally, the experimental results show that the above method, boasting advanced compression ratio and good recovery effect, is well suited for processing large data.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-014-9913-3