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18 December 2017 Research on compressive sensing reconstruction algorithm based on total variation model
Yu-xuan Gao, Huayan Sun, Tinghua Zhang, Lin Du
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060515 (2017) https://doi.org/10.1117/12.2286818
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.
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Yu-xuan Gao, Huayan Sun, Tinghua Zhang, and Lin Du "Research on compressive sensing reconstruction algorithm based on total variation model", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060515 (18 December 2017); https://doi.org/10.1117/12.2286818
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KEYWORDS
Reconstruction algorithms
Detection and tracking algorithms
Image compression
Image restoration
Surface plasmons
Compressed sensing
Detection theory
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