The Experts below are selected from a list of 21759 Experts worldwide ranked by ideXlab platform
Yushu Zhang - One of the best experts on this subject based on the ideXlab platform.
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Compressive Sensing
Secure Compressive Sensing in Multimedia Data Cloud Computing and IoT, 2019Co-Authors: Yushu Zhang, Yong Xiang, Leo Yu ZhangAbstract:Since Compressive Sensing (CS) theory has come into the world, it has been widely applied in many fields. It was claimed that both sampling and compression can be performed simultaneously to reduce the sampling rate at the expense of a high computation complexity at the reconstruction stage. By virtue of the sparsity, a signal, which is randomly projected at the encoder side, can be reconstructed by searching the optimal solution of an under determined linear system at the decoder side. In information security field, the CS can be utilized for multimedia data security, cloud computing security, internet of things (IoT) security, etc.
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a visually secure image encryption scheme based on Compressive Sensing
Signal Processing, 2017Co-Authors: Xiuli Chai, Zhihua Gan, Yiran Chen, Yushu ZhangAbstract:A novel visually secure image encryption scheme based on Compressive Sensing (CS) is proposed. Firstly, the plain image is transformed into wavelet coefficients, and then confused by a zigzag path and encrypted into a compressed cipher image using Compressive Sensing. Next, the cipher image is embedded into a carrier image, and finally a visually secure cipher image is obtained. SHA 256 hash function of the original image is generated to calculate the parameters for zigzag confusion and one-dimensional skew tent map, and the map is used to produce the measurement matrix for CS. Therefore, the proposed algorithm is highly sensitive to the plain image, and it can effectively withstand known-plaintext and chosen-plaintext attacks. Besides, our algorithm can achieve the image data security and image appearance security simultaneously, and the size of the cipher image and original image is equal, it does not require additional transmission bandwidth and storage space. Simulation results and performance analyses both demonstrate excellent encryption performance of the proposed encryption scheme. A visually secure image encryption scheme based on Compressive Sensing is proposed.The encryption approach is highly sensitive to the plain image.Simulation results and performance analyses verify the effectiveness of the proposed encryption algorithm.
Vasilis Ntziachristos - One of the best experts on this subject based on the ideXlab platform.
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guest editorial Compressive Sensing for biomedical imaging
IEEE Transactions on Medical Imaging, 2011Co-Authors: Ge Wang, Yoram Bresler, Vasilis NtziachristosAbstract:Compressive Sensing (CS) has seen impressive successes and fast growth over the past ten years, including applications in medical imaging. Applications of CS to magnetic resonance imaging (MRI) have been the earliest, most numerous, and most diverse, owing to the tremendous flexibility in designing the acquisition process and the pressing need that MRI has, as a slow acquisition modality, to reduce the sampling requirements.
Xiuli Chai - One of the best experts on this subject based on the ideXlab platform.
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a visually secure image encryption scheme based on Compressive Sensing
Signal Processing, 2017Co-Authors: Xiuli Chai, Zhihua Gan, Yiran Chen, Yushu ZhangAbstract:A novel visually secure image encryption scheme based on Compressive Sensing (CS) is proposed. Firstly, the plain image is transformed into wavelet coefficients, and then confused by a zigzag path and encrypted into a compressed cipher image using Compressive Sensing. Next, the cipher image is embedded into a carrier image, and finally a visually secure cipher image is obtained. SHA 256 hash function of the original image is generated to calculate the parameters for zigzag confusion and one-dimensional skew tent map, and the map is used to produce the measurement matrix for CS. Therefore, the proposed algorithm is highly sensitive to the plain image, and it can effectively withstand known-plaintext and chosen-plaintext attacks. Besides, our algorithm can achieve the image data security and image appearance security simultaneously, and the size of the cipher image and original image is equal, it does not require additional transmission bandwidth and storage space. Simulation results and performance analyses both demonstrate excellent encryption performance of the proposed encryption scheme. A visually secure image encryption scheme based on Compressive Sensing is proposed.The encryption approach is highly sensitive to the plain image.Simulation results and performance analyses verify the effectiveness of the proposed encryption algorithm.
Richard G Baraniuk - One of the best experts on this subject based on the ideXlab platform.
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Spectral Compressive Sensing
2012Co-Authors: Marco F Duarte, Richard G BaraniukAbstract:Compressive Sensing (CS) is a new approach to simultaneous Sensing and compression of sparse and compressible signals. A great many applications feature smooth or modulated signals that can be modeled as a linear combination of a small number of sinusoids; such signals are sparse in the frequency domain. In practical applications, the standard frequency domain signal representation is the discrete Fourier transform (DFT). Unfortunately, the DFT coefficients of a frequency-sparse signal are themselves sparse only in the contrived case where the sinusoid frequencies are integer multiples of the DFT’s fundamental frequency. As a result, practical DFT-based CS acquisition and recovery of smooth signals does not perform nearly as well as one might expect. In this paper, we develop a new spectral Compressive Sensing (SCS) theory for general frequency-sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter estimation algorithms from the field of spectrum estimation. Using peridogram and eigen-analysis based spectrum estimates (e.g., MUSIC), our new SCS algorithms significantly outperform the current state-of-the-art CS algorithms while providing provable bounds on the number of measurements required for stable recovery. I
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Spectral Compressive Sensing
2012Co-Authors: Marco F Duarte, Richard G BaraniukAbstract:Compressive Sensing (CS) is a new approach to simultaneous Sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its Compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter estimation algorithms from the field of spectral estimation. Using peridogram and eigenanalysis based spectral estimates (e.g., MUSIC), our new SCS algorithms significantly outperform the current state-of-the-art CS algorithms based on the DFT while providing provable bounds on the number of measurements required for stable recovery. I
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kronecker Compressive Sensing
IEEE Transactions on Image Processing, 2012Co-Authors: Marco F Duarte, Richard G BaraniukAbstract:Compressive Sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement systems for such signals is complicated by their higher dimensionality. In this paper, we propose the use of Kronecker product matrices in CS for two purposes. First, such matrices can act as sparsifying bases that jointly model the structure present in all of the signal dimensions. Second, such matrices can represent the measurement protocols used in distributed settings. Our formulation enables the derivation of analytical bounds for the sparse approximation of multidimensional signals and CS recovery performance, as well as a means of evaluating novel distributed measurement schemes.
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Spectral Compressive Sensing
2011Co-Authors: Marco F Duarte, Richard G BaraniukAbstract:Compressive Sensing (CS) is a new approach to simultaneous Sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its Compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins; when this is not the case, CS recovery performance degrades significantly. In this paper, we introduce the spectral CS (SCS) recovery framework for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter estimation algorithms from the field of spectral estimation. Using periodogram and line spectral estimation methods (specifically Thomson’s multitaper method and MUSIC), we demonstrate that SCS significantly outperforms current state-of-the-art CS algorithms based on the DFT while providing provable bounds on the number of measurements required for stable recovery
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distributed Compressive Sensing
arXiv: Information Theory, 2009Co-Authors: Dror Baron, Marco F Duarte, Michael B Wakin, Shriram Sarvotham, Richard G BaraniukAbstract:Abstract : Compressive Sensing is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. In this paper we introduce a new theory for distributed Compressive Sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. Our theoretical contribution is to characterize the fundamental performance limits of DCS recovery for jointly sparse signal ensembles in the noiseless measurement setting; our result connects single-signal, joint, and distributed (multi-encoder) Compressive Sensing. To demonstrate the efficacy of our framework and to show that additional challenges such as computational tractability can be addressed, we study in detail three example models for jointly sparse signals. For these models, we develop practical algorithms for joint recovery of multiple signals from incoherent projections. In two of our three models, the results are asymptotically best-possible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. DCS is immediately applicable to a range of problems in sensor arrays and networks.
Trac D Tran - One of the best experts on this subject based on the ideXlab platform.
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fast and efficient Compressive Sensing using structurally random matrices
IEEE Transactions on Signal Processing, 2012Co-Authors: Lu Gan, Nam Nguyen, Trac D TranAbstract:This paper introduces a new framework to construct fast and efficient Sensing matrices for practical Compressive Sensing, called Structurally Random Matrix (SRM). In the proposed framework, we prerandomize the Sensing signal by scrambling its sample locations or flipping its sample signs and then fast-transform the randomized samples and finally, subsample the resulting transform coefficients to obtain the final Sensing measurements. SRM is highly relevant for large-scale, real-time Compressive Sensing applications as it has fast computation and supports block-based processing. In addition, we can show that SRM has theoretical Sensing performance comparable to that of completely random Sensing matrices. Numerical simulation results verify the validity of the theory and illustrate the promising potentials of the proposed Sensing framework.
