Title: Signal Reconstruction Method Using Fusion Compressed Sensing Framework
Abstract:The greedy algorithms represented by the orthogonal matching pursuit( OMP) algorithm and the Compressive Sampling Matching Pursuit( CoSaMP),are practically used in the image processing based on the co...The greedy algorithms represented by the orthogonal matching pursuit( OMP) algorithm and the Compressive Sampling Matching Pursuit( CoSaMP),are practically used in the image processing based on the compressed sensing theory due to the low computation complexity and low times of measurements. However,there are the following disadvantages: 1) Relatively poor signal reconstruction accuracy; 2) High computation complexity and high times of measurements. In this paper,we propose a novel fusion framework of greedy algorithms,which combines the orthogonal matching pursuit( OMP) algorithm and the Compressive Sampling Matching Pursuit( CoSaMP) algorithm to obtain the novel fusion of matching pursuit( FMP). FMP firstly unites the two support sets from OMP and CoSaMP,and then selects the most appropriate atoms to achieve the secondary screening of the original two support sets,finally realizing the accurate signal reconstruction. The image reconstruction experiments and the stability of Framework show that,with the same test conditions,the proposed FMP algorithm can effectively improve the signal-to-noise ratio( SNR) with further improved reconstruction error. The reconstruction effects using proposed FMP are better than the other two greedy algorithms used separately for both high and low resolution images.Read More
Publication Year: 2014
Publication Date: 2014-01-01
Language: en
Type: article
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