Joint Compression and Encryption of Distributed Sources Based on Wavelet Transform and Semi-Tensor Product Compressed Sensing

Xu, Bo; Xie, Zhenglin; Zhang, Zhi; Han, Tong; Liu, Hong; Ju, Mingchi; Liu, Xuan

doi:10.1109/jsen.2022.3188537

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Improving the transmission security and efficiency of captured images from UAV has become our concern. The compressed sensing (CS) technique [6][7][8] can compress and encrypt UAV images simultaneously to achieve a low-dimensional matrix. The lowdimensional matrix can be transmitted safely and efficiently due to its incomprehensible meaning.…”

Section: Introductionmentioning

confidence: 99%

Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Jiang

Bai

2023

Applied Sciences

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The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will bring more noise. In the compressed sensing (CS) system, the projected SRE in the compressed measurement will bring a significant challenge to the recovery accuracy of the images. In this work, a new SRE structure is proposed. Following the new structure, a lower sparse representation error (LSRE) is achieved by eliminating groups of sparse representation. With the proposed LSRE modeling, a robust sensing matrix is designed to compress and encrypt the UAV images. Experiments for UAV images are carried out to compare the recovery performance of the proposed algorithm with the existing related algorithms. The results of the proposed algorithm reveal superior recovery accuracy. The new CS framework with the proposed sensing matrix to address the scenario of UAV images with large SRE is dominant.

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Section: Introductionmentioning

confidence: 99%

Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Jiang

Bai

2023

Applied Sciences

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Research on deep unfolding network reconstruction method based on scalable sampling of transient signals

Hu,

Niu,

Wang

et al. 2024

Sci Rep

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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.

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ADP-VFL: An Adaptive Differential Privacy Scheme for VPP Based on Federated Learning

Zhang,

Wen,

et al. 2024

ICC 2024 - IEEE International Conference on Communications

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Joint Compression and Encryption of Distributed Sources Based on Wavelet Transform and Semi-Tensor Product Compressed Sensing

Cited by 5 publications

References 31 publications

Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Research on deep unfolding network reconstruction method based on scalable sampling of transient signals

ADP-VFL: An Adaptive Differential Privacy Scheme for VPP Based on Federated Learning

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