Hu Jun, Niu Kai, Wang Yuanwen, Zhang Yongli, Liu Xuan
College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.
Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing, 100125, China.
Sci Rep. 2024 Nov 12;14(1):27733. doi: 10.1038/s41598-024-79466-0.
In order to solve the problems of long reconstruction time and low reconstruction accuracy of compressed sensing reconstruction algorithm in the measurement of transient signals, a deep unfolding network reconstruction method based on scalable sampling is proposed to achieve fast and high-quality reconstruction of transient signal under low number of measurements. Firstly, the measurement process of compressed sensing is embedded into the neural network to realize automatic design and optimization of the observation matrix, which can reduce the number of measurements. Secondly, scalable sampling is introduced into the measurement process of compressed sensing, which can realize the training of data with different sampling ratios in the same model. Finally, a deep unfolding network model is designed to reconstruct the transient signal, which not only realizes the interpretability of the reconstructed network, but also achieves fast and high-quality reconstruction of the transient signal under the low number of measurements. Experimental results show that compared with the traditional compressed sensing reconstruction algorithms, the proposed method can obtain high-quality reconstruction accuracy with lower measurement times, and the reconstruction time is greatly reduced. The algorithm in this paper also obtains good reconstruction results under different sampling ratios, which shows that the method in this paper has good adaptability and effectiveness.
为了解决压缩感知重建算法在瞬态信号测量中重建时间长和重建精度低的问题，提出了一种基于可扩展采样的深度展开网络重建方法，以在低测量次数下实现瞬态信号的快速高质量重建。首先，将压缩感知的测量过程嵌入到神经网络中，实现观测矩阵的自动设计和优化，从而减少测量次数。其次，将可扩展采样引入压缩感知的测量过程，能够在同一模型中实现不同采样率数据的训练。最后，设计了一个深度展开网络模型来重建瞬态信号，不仅实现了重建网络的可解释性，还在低测量次数下实现了瞬态信号的快速高质量重建。实验结果表明，与传统的压缩感知重建算法相比，该方法能够以更低的测量次数获得高质量的重建精度，并且重建时间大大缩短。本文算法在不同采样率下也获得了良好的重建结果，表明本文方法具有良好的适应性和有效性。
