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Image Reconstruction Based on Compressed Sensing Measurement Matrix Optimization Method

Caifeng Cheng1,2, Deshu Lin3,*

1 School of Electronics and Information, Yangtze University, Jingzhou, 434023, China
2 College of Engineering and Technology, Yangtze University, Jingzhou, 434020, China
3 School of Computer Science, Yangtze University, Jingzhou, 434023, China

* Corresponding Author: Deshu Lin. Email: email

Journal on Internet of Things 2020, 2(1), 47-54. https://doi.org/10.32604/jiot.2020.09117

Abstract

In this paper, the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied. The advantages and disadvantages of greedy reconstruction algorithm are analyzed. The disadvantages of signal sparsely are preset in this algorithm. The sparsely adaptive estimation algorithm is proposed. The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve. The sparse step size adaptive compressed sampling matching tracking algorithm is proposed. The improved algorithm selects the sparsely as the step size to select the support set atom, and the maximum correlation value. Half of the threshold culling algorithm supports the concentration of excess atoms. The experimental results show that the improved algorithm has better power and lower image reconstruction error under the same sparsely criterion, and has higher image reconstruction quality and visual effects.

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Cite This Article

APA Style
Cheng, C., Lin, D. (2020). Image reconstruction based on compressed sensing measurement matrix optimization method. Journal on Internet of Things, 2(1), 47-54. https://doi.org/10.32604/jiot.2020.09117
Vancouver Style
Cheng C, Lin D. Image reconstruction based on compressed sensing measurement matrix optimization method. J Internet Things . 2020;2(1):47-54 https://doi.org/10.32604/jiot.2020.09117
IEEE Style
C. Cheng and D. Lin, "Image Reconstruction Based on Compressed Sensing Measurement Matrix Optimization Method," J. Internet Things , vol. 2, no. 1, pp. 47-54. 2020. https://doi.org/10.32604/jiot.2020.09117



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