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Active Self-Paced Learning for Cost-Effective and Progressive Face Identification SCIE
期刊论文 | 2018 , 40 (1) , 7-19 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
WoS CC Cited Count: 9
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Abstract :

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/

Keyword :

Cost-effective model face identification self-paced learning incremental processing active learning

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GB/T 7714 Lin, Liang , Wang, Keze , Meng, Deyu et al. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (1) : 7-19 .
MLA Lin, Liang et al. "Active Self-Paced Learning for Cost-Effective and Progressive Face Identification" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40 . 1 (2018) : 7-19 .
APA Lin, Liang , Wang, Keze , Meng, Deyu , Zuo, Wangmeng , Zhang, Lei . Active Self-Paced Learning for Cost-Effective and Progressive Face Identification . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (1) , 7-19 .
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Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure SCIE PubMed Scopus
期刊论文 | 2018 , 19 (1) | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between the chemical structure and biological activities in the field of drug design and discovery. (1) Background: In the study of QSAR, the chemical structures of compounds are encoded by a substantial number of descriptors. Some redundant, noisy and irrelevant descriptors result in a side-effect for the QSAR model. Meanwhile, too many descriptors can result in overfitting or low correlation between chemical structure and biological bioactivity. (2) Methods: We use novel log-sum regularization to select quite a few descriptors that are relevant to biological activities. In addition, a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. (3) Results: Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. (4) Conclusions: Our proposed multiple linear regression with log-sum penalty is an effective technique for both descriptor selection and prediction of biological activity.

Keyword :

descriptor selection regularization log-sum QSAR biological activity

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GB/T 7714 Xia, Liang-Yong , Wang, Yu-Wei , Meng, De-Yu et al. Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure [J]. | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES , 2018 , 19 (1) .
MLA Xia, Liang-Yong et al. "Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure" . | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 19 . 1 (2018) .
APA Xia, Liang-Yong , Wang, Yu-Wei , Meng, De-Yu , Yao, Xiao-Jun , Chai, Hua , Liang, Yong . Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure . | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES , 2018 , 19 (1) .
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Denoising Hyperspectral Image With Non-i.i.d. Noise Structure EI SCIE Scopus
期刊论文 | 2018 , 48 (3) , 1054-1066 | IEEE TRANSACTIONS ON CYBERNETICS
WoS CC Cited Count: 3 SCOPUS Cited Count: 4
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Abstract :

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose an NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is then designed to infer the posterior of the proposed model. As substantiated by our experiments implemented on synthetic and real noisy HSIs, the proposed method performs more robust beyond the state-of-the-arts.

Keyword :

low-rank matrix factorization (LRMF) Hyperspectral image (HSI) denoising non independent and identically distributed (i.i.d.) noise modeling

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GB/T 7714 Chen, Yang , Cao, Xiangyong , Zhao, Qian et al. Denoising Hyperspectral Image With Non-i.i.d. Noise Structure [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) : 1054-1066 .
MLA Chen, Yang et al. "Denoising Hyperspectral Image With Non-i.i.d. Noise Structure" . | IEEE TRANSACTIONS ON CYBERNETICS 48 . 3 (2018) : 1054-1066 .
APA Chen, Yang , Cao, Xiangyong , Zhao, Qian , Meng, Deyu , Xu, Zongben . Denoising Hyperspectral Image With Non-i.i.d. Noise Structure . | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) , 1054-1066 .
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On Convergence Properties of Implicit Self-paced Objective EI SCIE Scopus
期刊论文 | 2018 , 462 , 132-140 | INFORMATION SCIENCES
SCOPUS Cited Count: 1
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Abstract :

Self-paced learning (SPL) is a new methodology that simulates the learning principle of humans/animals to start learning easier aspects of a learning task, and then gradually take more complex examples into training. This new-coming learning regime has been empirically substantiated to be effective in various computer vision and pattern recognition tasks. Recently, it has been proved that the SPL regime has a close relationship with a implicit self-paced objective function. While this implicit objective could provide helpful interpretations to the effectiveness, especially the robustness, insights under the SPL paradigms, there are still no theoretical results to verify such relationship. To this issue, we provide some convergence results on the implicit objective of SPL. Specifically, we will prove that the learning process of SPL always converges to critical points of this implicit objective under some mild conditions. This result verifies the intrinsic relationship between SPL and this implicit objective, and makes the previous robustness analysis on SPL complete and theoretically rational. (C) 2018 Elsevier Inc. All rights reserved.

Keyword :

Convergence Self-paced learning Non-convex optimization Machine learning

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GB/T 7714 Ma, Zilu , Liu, Shiqi , Meng, Deyu et al. On Convergence Properties of Implicit Self-paced Objective [J]. | INFORMATION SCIENCES , 2018 , 462 : 132-140 .
MLA Ma, Zilu et al. "On Convergence Properties of Implicit Self-paced Objective" . | INFORMATION SCIENCES 462 (2018) : 132-140 .
APA Ma, Zilu , Liu, Shiqi , Meng, Deyu , Zhang, Yong , Lo, SioLong , Han, Zhi . On Convergence Properties of Implicit Self-paced Objective . | INFORMATION SCIENCES , 2018 , 462 , 132-140 .
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Robust subspace clustering via penalized mixture of Gaussians EI SCIE Scopus
期刊论文 | 2018 , 278 , 4-11 | NEUROCOMPUTING
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Many problems in computer vision and pattern recognition can be posed as learning low-dimensional subspace structures from high-dimensional data. Subspace clustering represents a commonly utilized subspace learning strategy. The existing subspace clustering models mainly adopt a deterministic loss function to describe a certain noise type between an observed data matrix and its self-expressed form. However, the noises embedded in practical high-dimensional data are generally non-Gaussian and have much more complex structures. To address this issue, this paper proposes a robust subspace clustering model by embedding the Mixture of Gaussians (MoG) noise modeling strategy into the low-rank representation (LRR) subspace clustering model. The proposed MoG-LRR model is capitalized on its adapting to a wider range of noise distributions beyond current methods due to the universal approximation capability of MoG. Additionally, a penalized likelihood method is encoded into this model to facilitate selecting the number of mixture components automatically. A modified Expectation Maximization (EM) algorithm is also designed to infer the parameters involved in the proposed PMoG-LRR model. The superiority of our method is demonstrated by extensive experiments on face clustering and motion segmentation datasets. (C) 2017 Elsevier B. V. All rights reserved.

Keyword :

Mixture of Gaussians Subspace clustering Expectation maximization Low-rank representation

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GB/T 7714 Yao, Jing , Cao, Xiangyong , Zhao, Qian et al. Robust subspace clustering via penalized mixture of Gaussians [J]. | NEUROCOMPUTING , 2018 , 278 : 4-11 .
MLA Yao, Jing et al. "Robust subspace clustering via penalized mixture of Gaussians" . | NEUROCOMPUTING 278 (2018) : 4-11 .
APA Yao, Jing , Cao, Xiangyong , Zhao, Qian , Meng, Deyu , Xu, Zongben . Robust subspace clustering via penalized mixture of Gaussians . | NEUROCOMPUTING , 2018 , 278 , 4-11 .
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Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery EI SCIE Scopus
期刊论文 | 2018 , 40 (8) , 1888-1902 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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As a promising way for analyzing data, sparse modeling has achieved great success throughout science and engineering. It is well known that the sparsity/low-rank of a vector/matrix can be rationally measured by nonzero-entries-number (l(0) norm)/nonzero-singular-values-number (rank), respectively. However, data from real applications are often generated by the interaction of multiple factors, which obviously cannot be sufficiently represented by a vector/matrix, while a high order tensor is expected to provide more faithful representation to deliver the intrinsic structure underlying such data ensembles. Unlike the vector/matrix case, constructing a rational high order sparsity measure for tensor is a relatively harder task. To this aim, in this paper we propose a measure for tensor sparsity, called Kronecker-basis-representation based tensor sparsity measure (KBR briefly), which encodes both sparsity insights delivered by Tucker and CANDECOMP/PARAFAC (CP) low-rank decompositions for a general tensor. Then we study the KBR regularization minimization (KBRM) problem, and design an effective ADMM algorithm for solving it, where each involved parameter can be updated with closed-form equations. Such an efficient solver makes it possible to extend KBR to various tasks like tensor completion and tensor robust principal component analysis. A series of experiments, including multispectral image (MSI) denoising, MSI completion and background subtraction, substantiate the superiority of the proposed methods beyond state-of-the-arts.

Keyword :

tensor completion Tensor sparsity CANDECOMP/PARAFAC decomposition tucker decomposition multi-spectral image restoration

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GB/T 7714 Xie, Qi , Zhao, Qian , Meng, Deyu et al. Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (8) : 1888-1902 .
MLA Xie, Qi et al. "Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40 . 8 (2018) : 1888-1902 .
APA Xie, Qi , Zhao, Qian , Meng, Deyu , Xu, Zongben . Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (8) , 1888-1902 .
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Iterative quality enhancement via residual-artifact learning networks for low-dose CT EI Scopus SCIE
期刊论文 | 2018 , 63 (21) | Physics in Medicine and Biology
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Abstract :

© 2018 Institute of Physics and Engineering in Medicine. Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy may lead to excessive noise and streak artifacts in the conventional filtered back-projection reconstructed images. To address this issue, some deep convolutional neural network (ConvNet) based approaches have been developed for low-dose CT imaging inspired by the recent development of machine learning. Nevertheless, some of the image textures reconstructed by the ConvNet could be corrupted by the severe streaks, especially in ultra-low-dose cases, which could be close to prostheses and hamper diagnosis. Therefore, in this work, we propose an iterative residual-artifact learning ConvNet (IRLNet) approach to improve the reconstruction performance over the ConvNet based approaches. Specifically, the proposed IRLNet estimates the high-frequency details within the noise and then removes them iteratively; after eliminating severe streaks in the low-dose CT images, the residual low-frequency details can be processed through the conventional network. Moreover, the proposed IRLNet scheme can be extended for robust handling of quantitative dual energy CT/cerebral perfusion CT imaging, and statistical iterative reconstruction. Real patient data are used to evaluate the proposed IRLNet, and the experimental results demonstrate that the proposed IRLNet approach outperforms the previous ConvNet based approaches in reducing the image noise and streak artifacts efficiently at the same time as preserving edge details well, suggesting that the proposed IRLNet approach can be used to improve the CT image quality, especially in ultra-low-dose cases.

Keyword :

cerebral perfusion CT deep learning dual energy CT image restoration low dose CT statistical image reconstruction

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GB/T 7714 Wang, Yongbo , Liao, Yuting , Zhang, Yuanke et al. Iterative quality enhancement via residual-artifact learning networks for low-dose CT [J]. | Physics in Medicine and Biology , 2018 , 63 (21) .
MLA Wang, Yongbo et al. "Iterative quality enhancement via residual-artifact learning networks for low-dose CT" . | Physics in Medicine and Biology 63 . 21 (2018) .
APA Wang, Yongbo , Liao, Yuting , Zhang, Yuanke , He, Ji , Li, Sui , Bian, Zhaoying et al. Iterative quality enhancement via residual-artifact learning networks for low-dose CT . | Physics in Medicine and Biology , 2018 , 63 (21) .
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Robust Online Matrix Factorization for Dynamic Background Subtraction EI SCIE Scopus
期刊论文 | 2018 , 40 (7) , 1726-1740 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
WoS CC Cited Count: 3 SCOPUS Cited Count: 5
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Abstract :

We propose an effective online background subtraction method, which can be robustly applied to practical videos that have variations in both foreground and background. Different from previous methods which often model the foreground as Gaussian or Laplacian distributions, we model the foreground for each frame with a specific mixture of Gaussians (MoG) distribution, which is updated online frame by frame. Particularly, our MoG model in each frame is regularized by the learned foreground/background knowledge in previous frames. This makes our online MoG model highly robust, stable and adaptive to practical foreground and background variations. The proposed model can be formulated as a concise probabilistic MAP model, which can be readily solved by EM algorithm. We further embed an affine transformation operator into the proposed model, which can be automatically adjusted to fit a wide range of video background transformations and make the method more robust to camera movements. With using the sub-sampling technique, the proposed method can be accelerated to execute more than 250 frames per second on average, meeting the requirement of real-time background subtraction for practical video processing tasks. The superiority of the proposed method is substantiated by extensive experiments implemented on synthetic and real videos, as compared with state-of-the-art online and offline background subtraction methods.

Keyword :

online learning low-rank matrix factorization subspace learning Backgroun0d subtraction mixture of Gaussians

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GB/T 7714 Yong, Hongwei , Meng, Deyu , Zuo, Wangmeng et al. Robust Online Matrix Factorization for Dynamic Background Subtraction [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (7) : 1726-1740 .
MLA Yong, Hongwei et al. "Robust Online Matrix Factorization for Dynamic Background Subtraction" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40 . 7 (2018) : 1726-1740 .
APA Yong, Hongwei , Meng, Deyu , Zuo, Wangmeng , Zhang, Lei . Robust Online Matrix Factorization for Dynamic Background Subtraction . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (7) , 1726-1740 .
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Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network EI SCIE Scopus
期刊论文 | 2018 , 27 (5) , 2354-2367 | IEEE TRANSACTIONS ON IMAGE PROCESSING
WoS CC Cited Count: 3 SCOPUS Cited Count: 6
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Abstract :

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using alpha-expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Keyword :

Hyperspectral image classification Markov random fields convolutional neural networks

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GB/T 7714 Cao, Xiangyong , Zhou, Feng , Xu, Lin et al. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) : 2354-2367 .
MLA Cao, Xiangyong et al. "Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 27 . 5 (2018) : 2354-2367 .
APA Cao, Xiangyong , Zhou, Feng , Xu, Lin , Meng, Deyu , Xu, Zongben , Paisley, John . Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) , 2354-2367 .
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Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism SCIE Scopus
期刊论文 | 2017 , 36 (12) , 2487-2498 | IEEE TRANSACTIONS ON MEDICAL IMAGING | IF: 6.131
WoS CC Cited Count: 1
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Computed tomography (CT) image recovery from low-mAs acquisitions without adequate treatment is always severely degraded due to a number of physical factors. In this paper, we formulate the low-dose CT sinogram preprocessing as a standard maximum a posteriori (MAP) estimation, which takes full consideration of the statistical properties of the two intrinsic noise sources in low-dose CT, i.e., the X-ray photon statistics and the electronic noise background. In addition, instead of using a general image prior as found in the traditional sinogram recovery models, we design a new prior formulation to more rationally encode the piecewise-linear configurations underlying a sinogram than previously used ones, like the TV prior term. As compared with the previous methods, especially the MAP-based ones, both the likelihood/loss and prior/regularization terms in the proposed model are ameliorated in a more accurate manner and better comply with the statistical essence of the generation mechanism of a practical sinogram. We further construct an efficient alternating direction method of multipliers algorithm to solve the proposed MAP framework. Experiments on simulated and real low-dose CT data demonstrate the superiority of the proposed method according to both visual inspection and comprehensive quantitative performance evaluation.

Keyword :

noise modeling Computed tomography maximum a posteriori (MAP) regularization statistical model

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GB/T 7714 Xie, Qi , Zeng, Dong , Zhao, Qian et al. Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2017 , 36 (12) : 2487-2498 .
MLA Xie, Qi et al. "Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 36 . 12 (2017) : 2487-2498 .
APA Xie, Qi , Zeng, Dong , Zhao, Qian , Meng, Deyu , Xu, Zongben , Liang, Zhengrong et al. Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2017 , 36 (12) , 2487-2498 .
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