Dictionary Sparse Representation Research Articles

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains. Time-domain inversion has stronger stability and noise resistance compared to frequency-domain inversion. Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution. Therefore, the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution, stability, and noise resistance. The introduction of prior information constraints can effectively reduce ambiguity in the inversion process. However, the existing model-driven time-frequency joint inversion assumes a specific prior distribution of the reservoir. These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features. Therefore, this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain. The method is based on the impedance and reflectivity samples from logging, using joint dictionary learning to obtain adaptive feature information of the reservoir, and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity. The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation. We have finally achieved an inversion method that combines constraints on time-domain features and frequency features. By testing the model data and field data, the method has higher resolution in the inversion results and good noise resistance.

Online Sparse Representation Clustering for Evolving Data Streams.

Data stream clustering can be performed to discover the patterns underlying continuously arriving sequences of data. A number of data stream clustering algorithms for finding clusters in arbitrary shapes and handling outliers, such as density-based clustering algorithms, have been proposed. However, these algorithms are often limited in their ability to construct and merge microclusters by measuring the Euclidean distances between high-dimensional data objects, e.g., transferring valuable knowledge from historical landmark windows to the current landmark window, and exploiting evolving subspace structures adaptively. We propose an online sparse representation clustering (OSRC) method to learn an affinity matrix for evaluating the relationships among high-dimensional data objects in evolving data streams. We first introduce a low-dimensional projection (LDP) into sparse representation to adaptively reduce the potential negative influence associated with the noise and redundancy contained in high-dimensional data. Then, we take advantage of the l2,1 -norm optimization technique to choose the appropriate number of representative data objects and form a specific dictionary for sparse representation. The specific dictionary is integrated into sparse representation to adaptively exploit the evolving subspace structures of the high-dimensional data objects. Moreover, the data object representatives from the current landmark window can transfer valuable knowledge to the next landmark window. The experimental results based on a synthetic dataset and six benchmark datasets validate the effectiveness of the proposed method compared to that of state-of-the-art methods for data stream clustering.

A hybrid SVM and kernel function-based sparse representation classification for automated epilepsy detection in EEG signals

Automatic epilepsy detection based on electroencephalography (EEG) is crucial for advancing the diagnosis and treatment of epilepsy. In this paper, we propose a novel classification algorithm called SVM-KSRC, which differs from integrated learning approaches. The algorithm establishes a connection between support vector machine (SVM) and kernel sparse representation classification (KSRC) using support vectors. Specifically, we extract two types of features from the pre-processed EEG signals in this study. During the training phase, these features are utilized to train the SVM model and construct the kernel sparse representation dictionary. We differentiate the SVM part of the features of the test data to determine whether SVM or KSRC should be employed for classifying the test data. Our method is evaluated on two publicly available datasets: University of Bonn dataset and Neurology and Sleep Centre-New Delhi dataset. Through 10 times 10-fold cross validation, our method demonstrates superior performance in epilepsy detection when compared to existing machine learning methods. The experimental results demonstrate that SVM-KSRC is more effective compared to SVM and KSRC used separately. It achieves over 99% accuracy in all binary classification tasks and attains 100% accuracy in certain tasks. The source code is publicly available at https://github.com/Walkeraaa/SVM-KSRC.

Image restoration via joint low-rank and external nonlocal self-similarity prior

Recent studies have revealed that joint priors, such as joint sparsity and external nonlocal self-similarity (ENSS) prior and joint low-rank and sparsity prior, are extremely effective in various image inverse problems. Few works, however, make use of both low-rank and ENSS priors. With this in mind, in this paper we propose a new joint prior, namely LRENSS prior, which utilizes low-rank and ENSS priors jointly in a unified framework, and successfully adapt the proposed LRENSS prior to image restoration problems. Specifically, low-rank and ENSS priors are bridged by treating ENSS prior as dictionaries for structural sparse representation. Further, an elegant block coordinate descent method is developed to solve the corresponding optimization problem. The proposed LRENSS prior is validated on image denoising and image deblurring tasks. Experimental results illustrate that the proposed LRENSS prior has better performance than other state-of-the-art algorithms in both qualitative and quantitative assessments.

Motor imagery classification using sparse representations: an exploratory study

The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. sparse representation classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical mode decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of features. In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use multilayer perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Feature selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. SRC and SRMLP achieve an average accuracy of 75.95% and 82.51% respectively while the MLP is 72.38%, representing a gain between 4.93% and 14%. The use of EMD in relation to other feature processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP, etc.) have on average a performance close to other conventional models, but without surpassing them. The best sparse models achieve an average accuracy of 95.43% among the subjects in the base, while other model reach 98.33%. The improvement of self-adaptive mechanisms that respond efficiently to the user’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.

Bearing fault-induced feature enhancement via adaptive multi-band denoising model

To accurately extract the bearing fault-induced impulse features from the vibration signals corrupted by heavy noise and large-amplitude random impulses, an adaptive multi-band denoising model based on the Morlet wavelet filter and sparse representation is put forward. First, to locate the desired frequency band associated with fault components, the Morlet wavelet filter is employed to band-pass the signal from the perspective of the frequency-domain. Herein, an improved Protrugram-based index, termed as windowed envelope spectral kurtosis, is designed as the objective function to choose the optimal center frequency and the bandwidth of the Morlet wavelet filter. Furthermore, benefitting from the time-domain characteristics of the vibration signal, the in-band noise is eliminated by sparse representation. One of the critical parameters (resonance frequency) of the wavelet atom used in the sparse representation dictionary is directly taken as the center frequency of the Morlet wavelet filter, which makes full use of the information derived from the filter, and thus significantly improves the calculation efficiency. Finally, the recovery signal is demodulated by the Hilbert transform to extract the fault characteristic frequency. The effectiveness and superiority of the proposed method are demonstrated through a complete analysis of the simulated, experimental, and engineering signals, as well as a comparison with such prevalent methods as Kurtogram, individual sparse representation, and Morlet wavelet filter combined with the maximum correlation kurtosis deconvolution.

A novel image recognition using Fuzzy C-Means and content-based fabric image retrieval

ABSTRACT Due to the growth of image exploitation, there is a requirement of systemizing the images in several ways. Time consumption and accuracy are the most important problem in the CBIR method. Therefore, a Content-Based Fabric Image Retrieval (CBFIR) method is proposed in this paper that is based on texture and colour features extraction. The Fuzzy C-Means (FCM) algorithm creates primary clusters and the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm updates every cluster in the dictionary. To find the sparse representation dictionary, the test image is compared by the proposed method with the dictionaries generated. The input images are obtained from the fabric dataset. Several distance based similarity measures are utilized for comparison. It can be seen that the proposed method’s performance is sufficiently good concerning the rate of recall, accuracy, and precision with the values of 97%, 91%, and 88% respectively.

Partial discharge signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold

AbstractPartial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR‐DRRT) is proposed. TSR‐DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction.

SAR Image Target Recognition Method by Global and Local Dictionary Sparse Representation

ABSTRACT Target variant detection has been challenging for Synthetic Aperture Radar (SAR), and the performance of the target variant recognition needs to be further enhanced. SAR images are widely used in the field of target recognition and are important to support reconnaissance operations. This paper proposes a novel research approach to improve the efficiency of target recognition. First, the theory surrounding sparse representation and dictionary learning is presented. Secondly, a SAR image target recognition model is constructed according to the theory. Meanwhile, a dynamic joint sparse representation model is proposed based on multi-information and applied to SAR image target recognition when sparse representation is under consideration. Finally, experiments are set up to validate the proposed model. The results are presented as follows: 1. the recognition rate range of the sample SAR image target is 0.811–0.995 and 0.867–0.990, respectively, when the two cases, without registration processing and with registration processing, utilize the recognition method of dictionary learning and sparse representation. 2. with the increase in dictionary size, the average recognition rate of SAR images based on multi-information dynamic joint sparse representation also increases under the conditions of no logarithm transformation and median filtering and after logarithm transformation and median filtering are run. Then, the average recognition rate range is 0.63–0.9 and 0.65–0.96, respectively. Thus, the recognition rate is improved by 5%-10%. 3. the recognition approach based on the sparse representation of multi-information dynamic joint has distinct sparse degrees of SAR image recognition in the case of logarithmic transformation and median filtering when the sample image has been registered and not been registered. The relative average recognition rates were 0.950–0.970 and 0.955–0.977, respectively. The key contribution of the research is to offer a workable solution to the issues plaguing SAR target variant recognition and to improve the significant limitations in the state-of-the-art literature thoroughly.

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm.

In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

High-Resolution and Accurate RV Map Estimation by Spare Bayesian Learning

Range-velocity (RV) map (also called the range-Doppler map) is used in radar to detect targets and estimate the range and velocity of the targets. The conventional range-Doppler processing has its limits in attaining a high-resolution RV map with few samples. Moreover, the conventional range-Doppler processing may cause severe range migration problems and bias in the range estimation. In this paper, we propose to attain a high-resolution and accurate RV map with the sparse Bayesian learning (SBL) framework. Unlike the existing works that use the discrete Fourier transform to build the dictionary for sparse representation, we build a dictionary from a model of the wide-band frequency-modulated continuous-wave (FMCW) radar to avoid the model mismatch problem. This dictionary allows for the effective decomposition of the signal and the full utilization of the structure of the signal. In addition, based on the fast marginal likelihood maximization, we also propose a fast method to reduce the computational burden. Simulations show that the proposed method and the proposed fast method are superior in terms of resolution, accuracy, and maximum unambiguous velocity compared with the existing methods.

Hyperspectral Image Classification via Deep Structure Dictionary Learning

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods.

A Neurodynamic Approach to Nonsmooth Quaternion Distributed Convex Optimization With Inequality and Affine Equality Constraints

Quaternions have appeared in many practical fields, such as image processing and data mining and so on. This paper focuses on designing an efficient quaternion-valued neurodynamic approach (QNA) based on multi-agent systems to solve nonsmooth convex quaternion distributed optimization problems (QDOPs) with inequality and affine equality constraints. Each agent in the system cooperatively solves the minimum of the global objective function through the information of itself and its neighbors. At first, the related convex analysis of the quaternion field are given, which provides a theoretical basis for solving the nonsmooth QDOP. Then the considered QDOP is equivalently transformed by using the connectivity of the communication topology. After that, a distributed QNA is presented, where the adaptive controller is introduced to ensure that the penalty terms with respect to the inequality constraints can self-adjust according to the local states. It is shown that all agents reach a consensus while obtaining the optimal solution to the related QDOP. Finally, a numerical example and an application in dictionary sparse representation of color images based on quaternion are realized to intuitively describe the effectiveness and practical significance of the proposed QNA.

TU85. POLYGENIC RISK SCORE FOR CANNABIS USE DISORDER PREDICTS ACUTE INTOXICATING EFFECTS AND COGNITIVE DEFICITS INDUCED BY DELTA-9-TETRAHYDROCANNABINOL: A PILOT STUDY

Algorithms for learning overcomplete dictionaries for sparse signal representation are mostly iterative minimization methods that alternate between a sparse coding stage and a dictionary update stage. For most however, the notion of consistency of the learned quantities has not been addressed. Based on the observation that the observed signals can be approximated as a sum of rank one matrices, a new adaptive dictionary learning algorithm is proposed in this paper. It is derived via sequential adaptive penalized rank one matrix approximation where the ℓ1-norm is introduced as a penalty promoting sparsity. The proposed algorithm uses a block coordinate descent approach to consistently estimate the unknowns and has the advantage of having simple closed form solutions for both the sparse coding and dictionary update stages. The consistency properties of both the estimated sparse code and dictionary atom are provided. The performance of the proposed algorithm compared to some state of the art algorithms is illustrated on both simulated data and a real functional magnetic resonance imaging (fMRI) data set from a finger tapping experiment.

Convolutional Sparse Coding Using Pathfinder Algorithm-Optimized Orthogonal Matching Pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection

Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.

Discriminative Dictionary Learning-Based Sparse Classification Framework for Data-Driven Machinery Fault Diagnosis

Data-driven machinery fault diagnosis is important for smart industrial systems to guarantee safety and reliability. However, the conventional data-driven fault diagnosis methods rely on the expert-designed features, which greatly affect the diagnosis performances. Inspired by the sparse representation-based classification (SRC) methods which can learn discriminative sparse features adaptively, we propose a novel discriminative dictionary learning based sparse classification (DDL-SC) framework for data-driven machinery fault diagnosis. The DDL-SC framework can jointly learn a discriminative dictionary for sparse representation and an optimal linear classifier for pattern recognition, which bridges the gaps between two separate processes, dictionary learning and classifier training in traditional SRC methods. In the learning stage, to enhance the discriminability of dictionary learning, we introduce the discriminative sparse code error along with the reconstruction error and classification error into the optimization objective. In the recognition stage, we employ sparse codes of testing signals with respect to the learned discriminative dictionary as inputs of the learned classifier, and promote the recognition performance by connecting a binary hard thresholding operator with the classifier predictions. The effectiveness of DDL-SC is evaluated on the planetary bearing fault dataset and gearbox fault dataset, indicating that DDL-SC yields the recognition accuracies of 99.73% and 99.41%, respectively. Besides, the comparative studies prove the superiority of DDL-SC over several state-of-the-art methods for data-driven machinery fault diagnosis.

Data-driven pre-stack AVO inversion method based on fast orthogonal dictionary

Regularization techniques are commonly used to mitigate the ill-posed problems for pre-stack AVO (amplitude variation with offset) inversion. In recent years, a data-driven regularization method has successfully been applied to achieve desirable inversion accuracy. In contrast of assuming a certain distribution of the data, this method uses the dictionary learning algorithm to learn the structural characteristics of elastic parameters and then takes this information as a prior constraint in AVO inversion. However, in this method, KSVD (An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation) algorithm is used, which is of low computational efficiency and involves many parameters. In this paper, we propose a novel data-driven pre-stack inversion approach based on the orthogonal dictionary (ORTD) learning method. Because the atoms are orthogonal, the efficiency of dictionary operation is nearly 100 times higher than that of KSVD redundant dictionary. Instead of regulating the sparsity, atomic size and atomic length for the KSVD method, the proposed method depends on two parameters: threshold parameters and dictionary atomic length. Moreover, the parameter manipulation has little impacts on the operation efficiency for this approach. This greatly improves the applicability of the data-driven inversion method. We use model tests and field data applications to verify the performance of the proposed method.

Acoustic Source Localization in a Reverberant Environment Based on Sound Field Morphological Component Analysis and Alternating Direction Method of Multipliers

The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SF-MCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green’s function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation ( $T_{20}=174.30\,\,\text {ms}$ ). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise.

Sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive group-sparsity regularization.

In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.