Reconstruction Improvements on Compressed Sensing

Yan Zhang, Suxia Cui, Yonghui Wang

Abstract


This paper presents the design of Improvements on Reconstruction of Compressive Sensed images. The proposed techniques will improve the reconstruction time consumption. Those improvements use techniques including matrix simplification, multi-thread and GPU computations.  Implementing those improvements achieve gains on time consumption, compared to the baseline. This paper also presents a novel scheme of buffering steamed image (video) to achieve optimum performance.


Keywords


Compressive Sensing; GPU; multi-thread

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References


Morgan Stanley, “The Mobile Internet Report,” Dec. 2009

E. Candès and Terence Tao, “Near optimal signal recovery from random projections: Universal encoding strategies?” IEEE Trans. on Information Theory, vol. 52, No. 12, pp. 5406 - 5425, December 2006

D. Donoho, “Compressed sensing,” IEEE Trans. on Information Theory, vol. 52, No. 4, pp. 1289 - 1306, April 2006

V. K Goyal, A.K. Fletcher and Sundeep Rangan, “Compressive Samplingand Lossy Compression,” IEEE SIGNAL PROCESSING MAGAZINE, pp. 48-56, MARCH 2008

J. E. Fowler, S. Mun, and E. W. Tramel, “Block-Based Compressed Sensing of Images and Video,” Foundations and Trends in Signal Processing, vol. 4, No. 4, pp. 297-416, March 2012

NVIDIA Corporation, “GPU_Programming_Guide”, Version 2.5, 2006

Jill Reese and Sarah Zaranek, “GPU Programming in MATLAB,The Mathworks technical paper, 2011


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