Low storage space for compressive sensing: semi-tensor product approach

Wang, Jinming; Ye, Shiping; Ruan, Yue; Chen, Chaoxiang

doi:10.1186/s13640-017-0199-9

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…In terms of recovery quality, STP-CS is almost equal to conventional CS and CCS. Wang et al proposed a random-sampling method based on the STP-CS framework [32]. They used an improved iteratively reweighted least-squares (IRLS) algorithm to obtain the values of the sparse vector.…”

Section: Semitensor-product Compressed Sensingmentioning

confidence: 99%

Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

Liu

Peng

et al. 2020

Applied Sciences

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With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist–Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.

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Section: Semitensor-product Compressed Sensingmentioning

confidence: 99%

Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

Liu

Peng

et al. 2020

Applied Sciences

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“…In [95], [116], STP is adopted for image compressive sensing and an iterative optimization approach is used for reconstruction. The authors apply the discrete wavelet transform (DWT) on the image first.…”

Section: Related Workmentioning

confidence: 99%

“…In the end, the inverse discrete wavelet transform (IDWT) is applied to produce the reconstructed image. While the approach in [95], [116] produces good results at a high measurement rate, it is time consuming and needs many iterations. Applying a DWT before CS and an IDWT after CS reconstruction are inconvenient for compressive sensing.…”

Section: Related Workmentioning

confidence: 99%

Reconstruction of Compressive Sensed (CS) Images with Deep Equilibrium Model (DEQ) Based on Iterative Shrinkage-Thresholding Algorithm (ISTA)

Yu¹

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Compressive sensing (CS) is a signal processing technique that measures and compresses signal simultaneously and reconstructs it by finding solutions from underdetermined linear system. CS needs much smaller number of measurements than what is required by the Nyquist-Shannon sampling theorem. Traditional optimization algorithms require many iterations to reconstruct the signal and therefore it is time consuming. Accordingly, there is a need for quicker reconstruction methods. The objective of this thesis is to explore the application of neural network in CS inverse problem to get reconstruction quickly. This thesis has four main contributions. First, we use convolutional neural network (CNN) to improve efficiency of traditional CS reconstruction for sparse signal. The nonzero positions are directly found by CNN, then the rank deficiency model is converted into a full rank model and accurate reconstruction can be solved. Second, for the compressible signal such as image, we develop the semi-tensor product (STP) into a neural network. The measurement matrix is much smaller than that of traditional CS. The proposed STP-Net makes the sampling process convenient and provides good initial reconstruction without block artifacts. Third, inspired by iterative shrinkage-thresholding algorithm (ISTA), we build SPT-ISTA-Net. The model incorporates aggregated residual transformations (ResNeXt) to promote performance improvement and a squeeze-and-excitation (SE) block to enhance useful information. Fourth, to simplify the model structure, we built a deep equilibrium model dubbed as STP-DEQ-Net. The model uses one ISTA block as an implicit layer to implement an arbitrarily deep network. The model has competitive performance, and it has a trade-off between accuracy and computation. Multi-scale dilated convolutional layers are used to further improve performance.

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P-Tensor Product in Compressed Sensing

Peng

et al. 2019

IEEE Internet Things J.

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Low storage space for compressive sensing: semi-tensor product approach

Cited by 4 publications

References 36 publications

Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

Reconstruction of Compressive Sensed (CS) Images with Deep Equilibrium Model (DEQ) Based on Iterative Shrinkage-Thresholding Algorithm (ISTA)

P-Tensor Product in Compressed Sensing

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