A novel parallel reconstruction approach based on a semi-tensor product (STP) is proposed. A low-dimensional random matrix where the dimensions are 1/4.
May 1, 2022 · In large-scale applications of compressed sensing (CS), the time cost to reconstruct the original signal is too high.
Rapid Compressed Sensing Reconstruction: A Semi-tensor ...
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In reference [16] , the authors used the semi-tensor product to construct a measurement matrix, which reduced the storage space required for the measurement ...
Rapid compressed sensing reconstruction: A semi-tensor product approach. Jinming Wang, Zhenyu Xu, Zhangquan Wang, Sen Xu, Jun Jiang. Rapid compressed sensing ...
Fast reconstruction method for compressed sensing model with semi ...
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Abstract: To reduce the storage space of random measurement matrix and improve the reconstruction efficiency for compressed sensing (CS),a new sampling approach ...
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Rapid compressed sensing reconstruction: A semi-tensor product approach. Published:2020-02 Issue: Volume:512 Page:693-707. ISSN:0020-0255. Container-title ...
Nov 3, 2023 · By utilizing the semi-tensor product, this proposed method can compress high-dimensional signals using lower-dimensional measurement matrices, ...
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Oct 28, 2022 · This paper constructs a structured random matrix by the embedding operation of two seed matrices in which one is the incidence matrix of combinatorial designs.
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To demonstrate the effectiveness of the STP-based neural network, it is applied to image reconstruction to sample and recover larger images, ...
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Abstract—Semi-tensor product (STP) is developed into a neural network in this paper and applied to image compressive sensing.