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

Huaixin Chen, Jie Liu

Key Lab. of Information Processing, Southwest China Institute of Electronics Technology, Chengdu, P.R. China.

DOI: 10.4236/jsea.2012.512B026                

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.

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

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