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In order to comprehensively study the security of the Attribute-Based Encryption (ABE) scheme based on Learning With Errors (LWE) and test its ability to resist existing attacks, an analysis method for concrete security of ABE based on LWE is proposed. After consideration of the parameter restrictions caused by algorithms on lattices and noise expansion, this method applies the existing algorithms to solving LWE and the available program modules, and it can quickly provide the specific parameters that satisfy the scheme and estimate the corresponding security level. In addition, it can output the specific parameters that satisfy the pre-given security level. Finally, four existing typical schemes are analyzed by this method. Experiments show that the parameters are too large to be applied to practical applications.
Heterogeneous signcryption can ensure the confidentiality and unforgeability of information data between different cryptosystems systems. Security for the traditional Public Key Infrastructure (PKI) and Identity-Based Cryptosystem (IBC) two-way and anonymous heterogeneous signcryption scheme between PKI→IBC and IBC→PKI is analyzed. It is pointed out that PKI→IBC scheme and IBC→PKI scheme can not resist adversary attacks. The ciphertext can be decrypted under the adversary obtaining the ciphertext. To enhance security, a new PKI→IBC and IBC→PKI scheme is proposed, and then confidentiality and unforgeability of the scheme in the random oracle model on the basis of the assumptions of Computational Diffie-Hellman problem and Bilinear Diffie-Hellman problem is proved. The efficiency analysis shows that the new scheme has higher communication efficiency.
In a cloud database environment, data is usually encrypted and stored to ensure the security of cloud storage data. To overcome the shortcomings of encrypting the data that the query overhead is big, the cipher text sortings and query are not support, etc, this paper puts forward a kind of f - mOPE cryptograph database retrieval scheme. Based on the mOPE sequential encryption algorithm, the idea of binary sort tree data structure is used to generate plaintext one-to-one corresponding sequential coding. Data plaintext is converted into ciphertext storage based on the AES encryption scheme. The improved partial homomorphic encryption algorithm is used to improve the security of sequential encryption scheme. The security analysis and experimental results show that this scheme can not only resist statistical attack, but also reduce effectively server computing cost and improve database processing efficiency on the basis of guaranteeing data privacy.
Mobile network authentication protocol attacks continue to emerge. For the new 5G network protocol EAP-AKA', an EAP-AKA' security analysis method based on Lowe’s taxonomy is proposed. Firstly, 5G network, EAP-AKA', communication channel and adversary are formally modeled. Then Lowe authentication property is formally modeled. Using the TAMARIN prover, objectives of the security anchor key KSEAF are analyzed, such as Lowe’s taxonomy, perfect forward secrecy, confidentiality, etc. Four attack paths under 3GPP implicit authentication mode are discovered. Two improved schemes are proposed for the discovered security problems and their security is verified. Finally, the security of the two authentication protocols EAP-AKA’ and 5G AKA of the 5G network is compared, and it is found that the former is safer in terms of Lowe authentication property.
Considering that the existing attribute-based searchable encryption scheme lacks the authorization service to the cloud server, a multi-server searchable Ciphertext Polity Attribute Base Encryption (CP-ABE) scheme is proposed based on authorization. The scheme implements search services through a cloud filter server, cloud search server and cloud storage server cooperation mechanism. The users send the authorization information to the cloud filter server at once, then the server creates the authorization information; The cloud search server creates the trapdoor information based on the trapdoor information sent by the users. Without decrypting the cipher text, the cloud filter server can detect all the cipher texts. Multiple attribute authorities can be used to ensure efficient and fine-grained access under the premise of ensuring data confidentiality, solving the problem of leakage of data user keys. It can improve the data retrieval efficiency when people use the cloud server. Through security analysis, it is proved that the scheme can not steal sensitive information of data users while providing data retrieval services, and it can effectively prevent the leakage of data privacy.
In order to solve the problem that users can not request to exit during the bitcoin confusion process, an anonymous revocation scheme for Bitcoin confusion is proposed. The commitment is used to bind the user with its destination address. When the user requests to quit the shuffle service, a zero-knowledge proof of the commitment is made using the accumulator and the signatures of knowledge. Finally, the shuffled output address of the user who quits the service is modified to its destination address. Security analysis shows that the scheme satisfies the anonymity of the user who quits the service based on the double discrete logarithm problem and the strong RSA assumption, and can be implemented without modifying the current bitcoin system. The scheme allows at most n–2 users to exit in the confusion process of n (n≥10) honest users participation.
The definition and security models of partial blind signcryption scheme in heterogeneous environment between CertificateLess Public Key Cryptography (CLPKC)and Traditional Public Key Infrastructure (TPKI) are proposed, and a construction by using the bilinear pairing is proposed. Under the random oracle model, based on the assumptions of Computational Diffie-Hellman Problem(CDHP) and Modifying Inverse Computational Diffie-Hellman(MICDHP), the scheme is proved to meet the requirment of the unforgeability, confidentiality, partial blindness, and untraceability, undeniability. Finally, compared with the related scheme, the scheme increases the blindness and does not significantly increase the computational cost.
Data compression and decompression are widely used in modern communication and data transmission. However, how to decompress the damaged lossless compressed files is still a challenge. For the lossless data compression algorithm widely used in the general coding field, an effective method is proposed to repair the error and decompress and restore the corrupted LZSS files, and the theoretical basis is given. By using the residual redundancy left by the encoder to carry the check information, the method can repair the errors in LZSS compressed data without loss of any compression performance. The proposed method does not require additional bits or changes in coding rules and data formats, thus it is fully compatible with standard algorithms. That is, the data compressed by LZSS with error repair capability can still be decompressed by standard LZSS decoder. The experimental results verify the validity and practicability of the proposed algorithm.
Firstly, a network Dynamic Threat Attribute Attack Graph (DT-AAG) analysis model is constructed by using Attribute Attack Graph theory. On the basis of the comprehensive description of system vulnerability and network service-induced threat transfer relationship, a threat transfer probability measurement algorithm is designed in combination with Common Vulerability Scoring System (CVSS) vulnerability evaluation criteria and Bayesian probability transfer method. Secondly, based on the model, a Dynamic Threat Attribute Attack Graph generation Algorithm (DT-AAG-A) is designed by using the relationship between the threat and the vulnerability as well as the service. What’s more, to solve the problem that threat transfer loop existing in the generated attribute attack graph, the loop digestion mechanism is designed. Finally, the effectiveness of the proposed model and algorithm is verified by experiments.
In order to solve the problem of member information leakage in multi-party cooperative design of integrated circuits, a orthogonal obfuscation scheme of multi-hardware IPs core security protection is proposed. Firstly, the orthogonal obfuscation matrix generates orthogonal key data, and the obfuscated key of the hardware IP core is designed with the physical feature of the Physical Unclonable Function (PUF) circuit. Then the security of multiple hardware IP cores is realized by the orthogonal obfuscation state machine. Finally, the validity of orthogonal aliasing is verified using the ISCAS-85 circuit and cryptographic algorithm. The multi-hardware IP core orthogonal obfuscation scheme is tested under Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm process, the difference of Toggle flip rate between the correct key and the wrong key is less than 5%, and the area and power consumption of the larger test circuit are less than 2%. The experimental results show that orthogonal obfuscation can improve the security of multi-hardware IP cores, and can effectively defend against member information leakage and state flip rate analysis attacks.
Large integer multiplication is the most important part in public key encryption, which often consumes most of the computing time in RSA, ElGamal, Fully Homomorphic Encryption (FHE) and other cryptosystems. Based on Schönhage-Strassen Algorithm (SSA), a design of high-speed 768 kbit multiplier is proposed. As the key component, an 64k-point Number Theoretical Transform (NTT) is optimized by adopting parallel architecture, in which only addition and shift operations are employed and thus the processing speed is improved effectively. The large integer multiplier design is validated on Stratix-V FPGA. By comparing its results with CPU using Number Theory Library(NTL) and GMP library, the correctness of this design is proved. The results also show that the FPGA implementation is about eight times faster than the same algorithm executed on the CPU.
A Priority Scheduling scheme based on Adaptive Random Linear Network Coding (PSARLNC) is proposed, to avoid the high computation complexity of the scheduling scheme based on Random Linear Network Coding (RLNC) and the high feedback dependence of the network performance. The characteristics of the video stream and RLNC adapted to multicast are combined in this scheme. Compared with the traditional RLNC, the computation complexity of this scheme is reduced. After the initial transmission, the transmission slots left of the data packet are comprehensively considered in the subsequent data recovery phase, and the maximum transmission node of the destination node gain is selected to maximize data transmission. At the same time, the decoding probability is available according to the different receiving situations in each relay node. According to the decoding probability value, the scheduling priority is determined, and the forwarding is completed. The transmission of each node is adaptively adjusted, and the feedback information is effectively reduced. The simulation results show that the performance of this scheme is approached to the full-feedback scheme, with better performance in the reducing computational complexity and the decreasing feedback dependence.
In Software Defined Networks (SDN), latency and load are important factors for Controller Placement Problem (CPP). To reduce the transmission latency between controllers, the propagation latency and queuing latency of flow requests, and balance the controller load, a strategy on how to place and adjust the controller is proposed. It mainly includes Genetic Algorithm (GA) and Balanced Control Region Algorithm (BCRA) which are used to place the initial controller and one Algorithm of Dynamic Online Adjustment (ADOA), that is an online adjusting algorithm in term dynamic controlling. The above algorithms are all based on the network connectivity. The simulation results show that in initial controller placement situation, under the premise of guaranteeing the lower propagation latency, queue latency and controller transmission latency of flow request, when BCRA is deployed in small and medium-sized networks, its load balancing performance is similar to that of GA and superior to k-center and k-means algorithm; When GA is deployed in large networks, compared with BCRA, k-center and k-means, the load balancing rate increases averagely 49.7%. In the dynamic situation, ADOA can guarantee lower queuing delay and running time, and can still make the load balance parameter less than 1.54.
Recently, the mobile charging and data collecting by using Mobile Equipment (ME) in Wireless Sensor Networks (WSNs) is a hot topic. Existing studies determine usually the traveling path of ME according to the charging requirements of sensor nodes firstly, and then handle the data collecting. In this paper, charging requirement and data collecting are taken into consideration simultaneously. A one-to-many charging and data collecting model for ME is established with two optimization objectives, maximizing the total energy utilization and minimizing the average delay of data collecting. Due to the limited energy of the ME, the path planning strategy and the equalization charging strategy are designed. An improved multi-objective ant colony algorithm is proposed to solve the problem. Experiments show that the objective values, the number of Pareto solutions, the homogeneity of Pareto solutions and the distribution of Pareto solutions obtained by the proposed algorithm are all superior over NSGA-II algorithm.
Considering lacking of centralized and synergistic scheduling for time-slots and wavelength resources of the inter-TWDM-PONs, a novel Resource Allocation based on Bandwidth Prediction (RABP) strategy with software-defined centralized schedule is proposed. For intra-Optical Line Terminal (OLT), the BP neural network model based on Particle Swarm Optimization (PSO) algorithm is designed to predict the required bandwidth of each OLT in order to avoid the impact of delay between controller and OLT on real-time resource allocation. For inter-OLT, the slide cycle is dynamically set, and then the shared bandwidth of resource pool is counted in real-time according to the authorized information of optical network unit. In the process of wavelength scheduling, a wavelength scheduling mechanism with load balancing to achieve efficiently utilizing of wavelength resource is designed. The simulation results show that the proposed strategy not only effectively improves the utilization of channel resources, but also reducs the average packet delay.
In order to deal with the limited capacity of Virtualized Network Function (VNF), hardware acceleration resources are adopted in Software-Defined Networking and Network Function Virtualization (SDN/NFV) architecture. The deployment of hardware acceleration resources enables VNF to provide service guarantees for increasing data traffic. To overcome the ignorance of the requirements for VNF with high processing throughput in service chain in existing researches, a model for VNF placement with hardware acceleration support is proposed. Based on the bearing characteristics of hardware acceleration resources, the model prioritizes the reuse of acceleration resources in the switch under the optimal placement of VNF without acceleration to commercial servers. The mapping correlation between hardware acceleration resources and VNF is flexibly adjusted according to the requirements of network services. Simulation results show that the proposed model can bear more service flows and meet the high processing throughput needs of service chains than typical policies in the case of the same amount of resources, which improves effectively the resource utilization of the acceleration hardware deployed in the network.
A two-user single cell network employing Non-Orthogonal Multiple Access (NOMA) technique is studied. Accounting for time-varying channel fading and dynamic traffic arrival, a stochastic optimization issue is formulated, which aims to balance the queue delay of users and maximize the network total throughput. Based on Lyapunov optimization method, the closed-form optimal solution of the stochastic optimization issue is derived, and a low-complexity optimal delay equilibrium and power control method is proposed. The method is compared with a NOMA scheme using a non-optimal resource management method and a Time Division Multiple Access (TDMA) scheme with an optimal resource management method. Simulation results show that the proposed method can significantly improve network performance.
A novel scheme termed Hybrid Power Allocation Strategy (H-PAS), which is integrated with Statistical Channel State Information (S-CSI) and Instantaneous Channel State Information (I-CSI), is proposed for Non-Orthogonal Multiple Access (NOMA) based on cooperative relaying systems to achieve a better performance-complexity tradeoff. Simulation results demonstrate that, with the proposed H-PAS, on the one hand, NOMA shows distinct advantage on the sum-rate compared with conventional orthogonal multiple access techniques in which only the knowledge of S-CSI is available; On the other hand, NOMA reduces the signaling overhead and computational complexity at the expense of marginal sum rate degradation when compared with the cases in which only the knowledge of I-CSI is available for it.
In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%.
In order to improve the utilization of non-contiguous virtual array elements in the underdetermined DOA estimation of the coprime array, a DOA estimation method based on Toeplitz covariance matrix reconstruction is proposed. First, the virtual array element distribution characteristics of the matrix are analyzed from the perspective of the difference coarray. Additionally, according to the correspondence between the difference coarray and the wave path difference, the covariance matrix is extended to a Toeplitz array covariance matrix, of which some elements are zero. Then, the Toeplitz matrix is recovered to the full covariance matrix according to the low rank matrix completion theory. Finally, the root-MUSIC method is employed for the DOA estimation. Theoretical analysis and simulation results show that this method can increase the number of the resolvable signals by increasing the number of virtual array elements, eliminate the effect of the off-grid effect without discretization of the angle domain, and avoid regularization parameter selection. Therefore, the estimation accuracy and resolution are improved.
High Frequency Surface Wave Radar (HFSWR) utilizes electromagnetic wave diffracting along the earth to detect targets over the horizon. However, the increase of target distance decreases the received echo energy, and this degrades the detection capability. A joint domain matrix Constant False Alarm Rate (CFAR) detector is proposed to improve the detection performance. It employs the multi-dimensional information of signal in azimuth, Doppler velocity and range domain to detect target, and Log-Determinant Divergence (LDD) and Symmetrized Log-Determinant Divergence (SLDD) are used to replace the Riemannian Distance (RD) as the measure of distance. Finally, the experiment results show that the detector presented by the paper can improve the detection performance effectively.
With the development of light and small Unmanned Aerial Vehicles (UAV), the detection method of Mini SAR based on UAV platform brings a revolutionary impact on information acquisition mode. In this paper, a W-band Mini SAR system for UAV is proposed, including the system design proposal and composition, high linearity analog phase-locked frequency modulation, MilliMeter Wave (MMW) substrate integrated waveguide antenna, 3D integration and motion compensation methods to solve the key problems of Mini SAR. A W-band Mini SAR prototype is developed and the imaging test based on Multi-rotor UAV is proceeded. The results show that the resolution, volume and the weight of Mini SAR prototype is at the industry-leading level. A high SNR imaging with perfect focusing effect is obtained from flight test.
The radiometric calibration of Synthetic Aperture Radar (SAR) can establish a mapping relationship between SAR image and Radar Cross Section (RCS) of ground objects, which benefits the inversion of target physical properties, and further meets the needs of quantitative remote sensing. Compared with other wavebands, the reports about SAR works in S-band are rare. This paper focuses on the radiometric calibration of radar at S-band by using the known parameters of radar and plane. Firstly, the relationship between image pixel intensity and RCS of target is derived. Then, a detailed analysis on each error component is implemented, in which, the affection of antenna direction on radiometric calibration precision is given by the analytic expression. The analysis and simulation is propitious to error allocation during the design period. In addition, the mean RCS statistics of grass, road and calm water are given. The real data processing results show that a sufficient accuracy in 20° view angle can be achieved by using the radiometric calibration method.
Generally speaking, Three Dimension (3D) imaging of spinning space target is obtained by performing matrix factorization method on the scattering trajectories obtained from sequential radar images. Because of the errors of scattering center extraction and association, the 3D reconstruction accurate is reduced or even fail. In addition, the scattering center trajectory from turntable target consists with circle nature, which is inconsistent with the elliptic property of the scattering center trajectory obtained by optical geometry projection. To tackle these problems, this paper proposes a short time 3D reconstruction method of space target. Firstly, the retrieved trajectory is fitted with 2D circular nature to make the trajectory smooth and closer to the theoretical curve. Then the radar Line Of Sight (LOS) is estimated by multiple views and the circular curve is converted into elliptical curve by multiplying the coefficients calculated by the LOS. The 3D reconstruction can be obtained by performing matrix factorization method on elliptical curves. Finally, the simulations verify the effectiveness of the proposed method.
Because of restricted earth-based tracking network, Tracking, Telemetry and Command (TT&C) for lunar orbit micro-satellite is depended on Unified S/X Band (USB) antennas in China Chang’E-4 lunar exploration. Based on analysis of the geometry between relay satellite, micro-satellite and earth-based antennas during earth-moon transfer orbit, an applicable method to acquire delay observable through Same-Beam Interferometry (SBI) tracking by China deep space network is discussed. Benefited from more kinds of tracking resources and high accuracy orbit of relay satellite, delay observable for angular position measurement of micro-satellite in the order of 1 ns is obtained, which improves the micro-satellite orbit determination accuracy from 2 km to less than 1 km and improves orbit prediction accuracy from 6 km to 2 km. SBI tracking plays an important role in short arc orbit determination of micro-satellite.
Based on the analysis on the difference between vector tracking loop and scalar tracking loop on fault detection, it is pointed out that in vector receiver of Global Navigation Satellite System (GNSS), the detection statistic of Receiver Autonomous Integrity Monitoring (RAIM) algorithm is inaccurate because of the influence of noise, and the propagation of fault information in the loop makes it difficult to identify the fault source. To solve the problems, a double loop tracking structure based on pre-filter is proposed after modifying the structure of vector receiver. In the new receiver, the influence of noise is reduced by pre-filter based on cubature Kalman filtering algorithm, and the fault information is prevented from propagating to each other by switching the loop. Finally, the method is verified by simulation. Simulation results show that the improved vector receiver not only greatly reduces the mean and variance of RAIM detection statistics, but also improves the accuracy of fault identification. Thus, the performance of RAIM is significantly improved.
In order to improve the secure transmission performance of dual-polarized satellite communication, a secure transmission method based on Double Layer Multi-Parameter Weighted-type FRactional Fourier Transform (DL-MPWFRFT) is proposed. Two orthogonally polarization amplitude-phase modulation branch signals are processed by DL-MPWFRFT, and then uploaded to the satellite channel by digital-to-analog conversion and radio frequency processing. The simulation results show that the method rotates and spreads the constellation of the transmitted signal, improves the anti-scanning capability, and increases the anti-interception performance of the system meanwhile increasing the system capacity.
Correlation Filters (CF) are efficient in visual tracking, but their performance is badly affected by boundary effects. Focusing on this problem, the adaptive regularized correlation filters for visual tracking based on sample quality estimation are proposed. Firstly, the proposed algorithm adds spatial regularization matrix to the training process of the filters, and constructs color and gray histogram templates to compute the sample quality factor. Then, the regularization term adaptively changes with the sample quality coefficient, so that the samples of different quality are subject to different degrees of punishment. Then, by thresholding the sample quality coefficient, the tracking results and model update strategy are optimized. The experimental results on OTB2013 and OTB2015 indicate that, compared with the state-of-the-art tracking algorithm, the average success ratio of the proposed algorithm is the highest. The success ratio is raised by 9.3% and 9.9% contrasted with Spatially RegularizeD Correlation Filters(SRDCF) algorithm respectively on OTB2013 and OTB2015.
In order to improve the recognition rate of banknotes, the improved banknote recognition algorithm based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, the algorithm constructs a deep convolution layer by integrating transfer learning, Leaky-Rectified Liner Unit (Leaky ReLU) function, Batch Normalization(BN) and multi-level residual unit that perform stable and fast feature extraction and learning on input different size banknotes. Secondly, a fixed-size output representation of the extracted banknote features is obtained by using the improved multi-level spatial pyramid pooling algorithm. Finally, the banknote classification is implemented by the full connection layer and the softmax layer of the network. The experimental results show that the proposed algorithm can effectively improve the recognition rate of banknotes, and has better generalization ability and robustness than the traditional banknote classification method. Meanwhile, the algorithm can meet the real-time requirements of the banknote sorting system.
The structure of Tree-Augmented Naïve Bayes (TAN) forces each attribute node to have a class node and a attribute node as parent, which results in poor classification accuracy without considering correlation between each attribute node and the class node. In order to improve the classification accuracy of TAN, firstly, the TAN structure is proposed that allows each attribute node to have no parent or only one attribute node as parent. Then, a learning method of building the tree-like Bayesian classifier using a decomposable scoring function is proposed. Finally, the low-order Conditional Independency (CI) test is applied to eliminating the useless attribute, and then based on improved Bayesian Information Criterion (BIC) function, the classification model with acquired the parent node of each attribute node is established using the greedy algorithm. Through comprehensive experiments, the proposed classifier outperforms Naïve Bayes (NB) and TAN on multiple classification, and the results prove that this learning method has certain advantages.
To solve the problems of Apriori algorithm and FP-Growth algorithm in the process of mining the maximal frequent itemsets, which refer to inefficient operation, high memory consumption, difficulty in adapting to the process of dense datasets, and affecting the time-effectiveness of large data value mining, this paper proposes a maximal frequent itemsets mining algorithm based on adjacency table. The algorithm only needs to traverse the database once and adopts the hash table to store the adjacency table, which reduces the memory consumption. Theoretical analysis and experimental results show that the algorithm has lower time and space complexity and improves the mining rate of maximal frequent itemsets, especially when dealing with dense datasets.
The multi-threshold image segmentation of the classical 2D maximal between-cluster variance method has deficiencies such as large computation, long calculation time, low segmentation precision and so on. A multi-threshold segmentation of 2D Otsu based on improved Adaptive Differential Evolution (JADE) algorithm is proposed. Firstly, in order to enhance the quality of the initialized population and improve the adaptability of the control parameters, the chaotic mapping mechanism is integrated into the JADE algorithm. Furthermore, the optimal segmentation threshold of 2D Otsu multi-threshold image is solved by improved JADE algorithm. Finally, the algorithm is compared with multi-threshold image segmentation method of 2D Otsu based on Differential Evolution (DE), JADE, Improved Differential Evolution with Adaptive Sinusoidal Parameters (LSHADE-cnEpSin) and Enhanced Adaptive Differential Transformation Differential Evolution (EFADE) algorithm. The experimental results show that compared with the other four algorithms, the multi-threshold image segmentation of 2D Otsu based on the improved JADE algorithm has a significant improvement in terms of segmentation speed and accuracy.
In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed. The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator. Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable. Experimental results show that, compared with the CS reconstruction algorithm via non-local low rank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases by 2.49 dB at most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.
Monthly Journal Founded in 1979
The Source Journal of EI Compendex The Source Journal of ESCI Database
Competent unit：Authorized by CAS
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ISSN 1009-5896 CN 11-4494/TN
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