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M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Processing Magazine, vol. 2, no. 25, pp. 83–91, 2008.

has been cited by the following article:

• TITLE: A Distributed Compressed Sensing for Images Based on Block Measurements Data Fusion

  AUTHORS: Huaixin Chen, Jie Liu

  KEYWORDS: distributed CS for image; information fusion; pattern recognition; network communication

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 25, 2013

  ABSTRACT: Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a novel method called distributed compressed sensing for image using block measurements data fusion. Firstly, original image is divided into small blocks and each block is sampled independently using the same measurement operator, to obtain the smaller encoded sparser coefficients and stored measurements matrix and its vectors. Secondly, original image is reconstructed using the block measurements fusion and recovery transform. Finally, several numerical experiments demonstrate that our method has a much lower data storage and calculation cost as well as high quality of reconstruction when compared with other existing schemes. We believe it is of great practical potentials in the network communication as well as pattern recognition domain.