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Improvement of Compressive Sampling and Matching Pursuit Algorithm Based on Double Estimation

Guiling Sun, Yangyang Li, Haojie Yuan, Jingfei He, and Tianyu Geng
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, China
Abstract—Compression sampling matching pursuit algorithm (CoSaMP) is widely applied to image reconstruction owing to its high precision of reconstruction, robustness, and simple operation. In this paper, we propose a new method, the CoSaMP based on double estimation, to properly overcome the shortcomings in CoSaMP for choosing too much optional atoms and imprecise choice. The concept of the maximum estimation, which is called maxValue, is proposed as a key point. The maxValueis calculated from the largest relevant 2k atoms of selected atoms in each iteration using least square solution method. At the next step, the maxValue is regard as a selection condition for the optional atoms to decrease the number of candidate atoms and increase its accuracy. The simulation results show that this algorithm can precisely reconstruct the original signal. Under the same sampling rate, compared to the CoSaMP, the proposed method can greatly improve the PSNR and reconstruction performance.

Index Terms—Compressed sensing, double estimation, sparse reconstruction, atomic precision filter

Cite: Guiling Sun, Yangyang Li, Haojie Yuan, Jingfei He, and Tianyu Geng, “Improvement of Compressive Sampling and Matching Pursuit Algorithm Based on Double Estimation," Journal of Communications, vol. 11, no. 6, pp. 573-578, 2016. Doi: 10.12720/jcm.11.6.573-578
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