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Research on Compressive Sensing Reconstruction Algorithm Based on Total Variation Model

机译：基于总变异模型的压缩感知重构算法研究

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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.
机译：突破性奈奎斯特采样定理的压缩传感提供了强大的理论，使图像信号的压缩采样可以同时进行。在使用压缩感测理论的传统成像程序中，它不仅可以减少存储空间，而且可以大大减少对探测器分辨率的需求。利用图像信号的稀疏性，通过求解逆重构的数学模型，实现了超分辨率成像。重建算法是压缩感知的最关键部分，在很大程度上决定了图像重建的准确性。基于全变异模型的重建算法更适合于二维图像的压缩重建。 ，可以获得更好的边缘信息。为了验证算法的性能，对基于电视重构算法的重构算法在不同编码方式下的重构结果进行了仿真分析。分析了基于电视的可重构算法在不同编码方式下的重构效果，验证了算法的稳定性。本文对相同编码模式下的典型重构算法进行了比较和分析。在最小总方差算法的基础上，增加了增广的拉格朗日函数项，并通过交变方向法求解了最优值。实验结果表明，该重构算法与传统的基于电视的经典算法相比具有很大的优势，在低测量速率下可以快速，准确地恢复目标图像。

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