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Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling EI SCIE
期刊论文 | 2021 , 14 , 1628-1644 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Abstract :

In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs and the embedded noise, leading to suboptimal performance in practice. In this article, we propose a novel HSI restoration method by fully considering the intrinsic image structures and the complex noise characteristics. For HSIs, the global correlation is captured by the Kronecker-basis-representation-based tensor low-rankness measure, which integrates the insights delivered by both CP and Tucker decompositions; the local regularity is depicted by a plug-and-play spatial-spectral convolutional neural network with strong fitting ability to complex image features. For realistic noise, its statistical characteristics are encoded by a nonidentical and nonindependent distributed mixture of Gaussians distribution with flexible fitting capability. Then, we incorporate these image and noise priors into a probabilistic model based on the maximum a posteriori principle, and develop a solving scheme by combining expectation-maximization and alternating direction method of multipliers. Extensive experimental results on both simulated and real scenarios demonstrate the effectiveness of the proposed method and its superiority over the compared state-of-the- arts. © 2008-2012 IEEE.

Keyword :

Restoration Maximum principle Image reconstruction Image segmentation Complex networks Spectroscopy Convolutional neural networks

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GB/T 7714 Ma, Tian-Hui , Xu, Zongben , Meng, Deyu et al. Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling [J]. | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021 , 14 : 1628-1644 .
MLA Ma, Tian-Hui et al. "Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling" . | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021) : 1628-1644 .
APA Ma, Tian-Hui , Xu, Zongben , Meng, Deyu , Zhao, Xi-Le . Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling . | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021 , 14 , 1628-1644 .
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Learning to Mutate for Differential Evolution CPCI-S
会议论文 | 2021 , 1-8 | IEEE Congress on Evolutionary Computation (IEEE CEC)
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Abstract :

Adaptive parameter control and mutation operator selection are two important research avenues in differential evolution (DE). Existing works consider the two avenues independently. In this paper, we propose to unify the two modules and develop a unified parameterized mutation operator. With different settings of the parameters, different mutation operators can be retrieved. Further, the settings of the parameters closely relate to the control parameters of the DE. By determining the parameters we can achieve adaptive parameter control and mutation operator selection simultaneously. We propose to use a neural network to output the parameters and learn the network parameter by the natural evolution strategies algorithm under the consideration of modeling the evolution process as a Markov Decision Process. Experimental results on the CEC 2018 test suite show that the proposed method performs significantly better than traditional DEs with different operators and an advanced adaptive DE. We further analyze the time complexity and population diversity of the proposed method. The analysis shows that our method can achieve a balanced exploration and exploitation with a properly learned network.

Keyword :

Markov Descision Process Adaptive Parameter Control Differential Evolution Adaptive Operator Selection

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GB/T 7714 Zhang, Haotian , Sun, Jianyong , Xu, Zongben . Learning to Mutate for Differential Evolution [C] . 2021 : 1-8 .
MLA Zhang, Haotian et al. "Learning to Mutate for Differential Evolution" . (2021) : 1-8 .
APA Zhang, Haotian , Sun, Jianyong , Xu, Zongben . Learning to Mutate for Differential Evolution . (2021) : 1-8 .
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Building large-scale density model via a deep-learning-based data-driven method EI SCIE
期刊论文 | 2021 , 86 (1) , M1-M15 | GEOPHYSICS
WoS CC Cited Count: 1
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As a rock-physics parameter, density plays a crucial role in lithology interpretation, reservoir evaluation, and description. However, density can hardly be directly inverted from seismic data, especially for large-scale structures; thus, additional information is needed to build such a large-scale model. Usually, well log data can be used to build a large-scale density model through extrapolation; however, this approach can only work well for simple cases and it loses effectiveness when the medium is laterally heterogeneous. We have adopted a deep-learning-based method to build a large-scale density model based on seismic and well log data. The long short-term memory network is used to learn the relation between seismic data and large-scale density. Except for the data pairs directly obtained from well logs, many velocity and density models randomly generated based on the statistical distributions of well logs are also used to generate several pairs of seismic data and the corresponding large-scale density. This can greatly enlarge the size and diversity of the training data set and consequently leads to a significant improvement of the proposed method in dealing with a heterogeneous medium even though only a few well logs are available. Our method is applied to synthetic and field data examples to verify its performance and compare it with the well extrapolation method, and the results clearly display that the proposed method can work well even though only a few well logs are available. Especially in the field data example, the built large-scale density model of the proposed method is improved by 11.9666 dB and 0.6740, respectively, in peak signal-tonoise ratio and structural similarity compared with that of the well extrapolation method.

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GB/T 7714 Gao, Zhaoqi , Li, Chuang , Zhang, Bing et al. Building large-scale density model via a deep-learning-based data-driven method [J]. | GEOPHYSICS , 2021 , 86 (1) : M1-M15 .
MLA Gao, Zhaoqi et al. "Building large-scale density model via a deep-learning-based data-driven method" . | GEOPHYSICS 86 . 1 (2021) : M1-M15 .
APA Gao, Zhaoqi , Li, Chuang , Zhang, Bing , Jiang, Xiudi , Pan, Zhibin , Gao, Jinghuai et al. Building large-scale density model via a deep-learning-based data-driven method . | GEOPHYSICS , 2021 , 86 (1) , M1-M15 .
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Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion EI
期刊论文 | 2021 , 87 (1) | Geophysics
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Seismic acoustic-impedance (AI) inversion, which estimates the AI of the reservoir from seismic and other geophysical data, is a type of nonlinear inverse problem that faces the local minima issue during optimization. Without requiring an accurate initial model, global optimization methods have the ability to jump out of local minima and search for the optimal global solution. However, the low-efficiency nature of global optimization methods hinders their practical applications, especially in large-scale AI inversion problems (AI inversion with a large number of traces). We propose a new intelligent seismic AI inversion method based on global optimization and deep learning. In this method, global optimization is used to generate datasets for training a deep learning network and it is used to first accelerate and then surrogate global optimization. In other words, for large-scale seismic AI inversion, global optimization only inverts the AI model for a few traces, and the AI models of most traces are obtained by deep learning. The deep learning architecture that we used to map from seismic trace to its corresponding AI model is established based on U-Net. Because the time-consuming global optimization inversion procedure can be avoided for most traces, this method has a significant advantage over conventional global optimization methods in efficiency. To verify the effectiveness of the proposed method, we compare its performance with the conventional global optimization method on 3D synthetic and field data examples. Compared with the conventional method, the proposed method only needs about one-tenth of the computation time to build AI models with better accuracy. © 2022 Society of Exploration Geophysicists.

Keyword :

Acoustic impedance Efficiency Deep learning Inverse problems Global optimization Seismology

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GB/T 7714 Gao, Zhaoqi , Yang, Wei , Tian, Yajun et al. Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion [J]. | Geophysics , 2021 , 87 (1) .
MLA Gao, Zhaoqi et al. "Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion" . | Geophysics 87 . 1 (2021) .
APA Gao, Zhaoqi , Yang, Wei , Tian, Yajun , Li, Chuang , Jiang, Xiudi , Gao, Jinghuai et al. Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion . | Geophysics , 2021 , 87 (1) .
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致密油气介质中波的控制方程
期刊论文 | 2021 , 51 (3) , 353-363 | 中国科学(地球科学)
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致密油气介质是一种特殊的多孔介质,在油气勘探开发中占有重要地位.文章建立致密油气介质中波的控制方程,该方程较一般多孔介质波方程,形式大为简化,可用于由地震数据进行物性参数反演.文章首先简单介绍从孔隙尺度上流体的运动方程和固体骨架颗粒运动方程出发,利用体平均定理推导出完备的Biot方程的思路与结果,厘清了其中使用的假设条件.其次以岩石物理测试结果为基础,详细分析了致密油气介质中渗透率的时间变化率的性质,进而利用Kozeny-Carman方程,研究了孔隙度的时间变化率的性质,提出了致密油气介质中孔隙度作为状态变量的一个合理假设.在此基础上,从完备的Biot方程出发,推导出了致密油气介质中波的控制方程.该方程与经典的“弥散黏滞方程”在形式上一致,通过对比,得到了弥散黏滞方程中的系数和有明确物理意义的介质物性参数间的解析关系式.文中通过数值模拟验证了所建立的方程的正确性.基于所建立的方程,研究了单一致密夹层的地震波反射和透射特性.数值模拟结果表明夹层的厚度和衰减特性对于地震波的反射和透射有显著影响,这一认识对油气探测有重要意义.

Keyword :

致密油气 渗透率 孔隙度 完备的Biot方程 体平均定理 波动方程

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GB/T 7714 高静怀 , Weimin HAN , 何彦斌 et al. 致密油气介质中波的控制方程 [J]. | 中国科学(地球科学) , 2021 , 51 (3) : 353-363 .
MLA 高静怀 et al. "致密油气介质中波的控制方程" . | 中国科学(地球科学) 51 . 3 (2021) : 353-363 .
APA 高静怀 , Weimin HAN , 何彦斌 , 赵海霞 , 李辉 , 张懿洁 et al. 致密油气介质中波的控制方程 . | 中国科学(地球科学) , 2021 , 51 (3) , 353-363 .
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A distribution independence based method for 3D face shape decomposition EI SCIE
期刊论文 | 2021 , 210 | COMPUTER VISION AND IMAGE UNDERSTANDING
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Decomposing a 3D face shape into different attribute components is usually beneficial to many applications, such as 3D face generation and attribute transfer. In this paper, we propose a novel method to learn independent latent representations of 3D face shapes to decompose a given 3D face shape into identity and expression components. We assume that the identity describes the intrinsic geometry of a face while the expression captures the extrinsic one, and thus they are independent of each other. Based on this assumption, we encode a 3D face shape into its identity and expression representations by a variational inference framework, that is equipped with Graph Convolutional Networks (GCN). Furthermore, we introduce a binary discriminator to enforce the latent representations of identity and expression to be distribution independent by adversarial learning. Both qualitative and quantitative experimental results show that the proposed approach can achieve state-of-the-art results in 3D face shape decomposition. Extensive applications on 3D facial expression transfer, 3D face recognition, and 3D face generation further demonstrate that the proposed method can achieve visually better transferred expressions, purer identity representations, and more diverse 3D face shapes, compared with existing state-of-the-art methods.

Keyword :

Graph convolutional network 3D face shape decomposition Variational inference Distribution independence

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GB/T 7714 Yu, Cuican , Zhang, Zihui , Li, Huibin et al. A distribution independence based method for 3D face shape decomposition [J]. | COMPUTER VISION AND IMAGE UNDERSTANDING , 2021 , 210 .
MLA Yu, Cuican et al. "A distribution independence based method for 3D face shape decomposition" . | COMPUTER VISION AND IMAGE UNDERSTANDING 210 (2021) .
APA Yu, Cuican , Zhang, Zihui , Li, Huibin , Sun, Jian , Xu, Zongben . A distribution independence based method for 3D face shape decomposition . | COMPUTER VISION AND IMAGE UNDERSTANDING , 2021 , 210 .
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Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network SCIE
期刊论文 | 2021 , 54 (9) , 645-664 | SPECTROSCOPY LETTERS
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It is of great social value and cultural and technological innovation demonstration value to carry out the research on the scientific identification method of painting and calligraphy works of art, and it is of great effect to the trade, collection, and protection of painting and calligraphy works of art. Spectral imaging, as an information acquisition method of attribute and visual synchronous perception, can be used for painting and calligraphy identification. In particular, through hyperspectral imaging and data analyses, we can identify the pigment ink used in painting, judge the printing characteristics, and find the painting information invisible to human eyes, to comprehensively judge the authenticity and abnormality of painting. However, due to its lack of matching painting and calligraphy identification model and algorithm, as well as special painting and calligraphy atlas database support, it is difficult to be competent for rapid and accurate identification in practice. Because of the above problems, in this paper, it is simulated the expert identification process for artificial intelligence analysis and modeling, adopts the idea of combining hyperspectral imaging and Atlas intelligent learning, proposes a method of atlas feature extraction for calligraphy and painting identification, and designs and studies convolution neural network(CNN) based on atlas feature, traditional image feature, and the mixed feature of atlas and image, to judge the authenticity of calligraphy and painting, the author and so on. The actual test results show that the convolution neural network based on the atlas features is the best, the author classification accuracy and authenticity identification rate in the test sample set are more than 96.5%, and it can also be seen that in the pseudo color image data, adding multivariate spectral features can greatly improve the accuracy significantly.

Keyword :

atlas features analysis convolution neural network spectral imaging Painting and calligraphy identification

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GB/T 7714 Tang, Xingjia , Zhang, Penchang , Du, Jian et al. Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network [J]. | SPECTROSCOPY LETTERS , 2021 , 54 (9) : 645-664 .
MLA Tang, Xingjia et al. "Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network" . | SPECTROSCOPY LETTERS 54 . 9 (2021) : 645-664 .
APA Tang, Xingjia , Zhang, Penchang , Du, Jian , Xu, Zongben . Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network . | SPECTROSCOPY LETTERS , 2021 , 54 (9) , 645-664 .
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A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation CPCI-S
会议论文 | 2021 , 12903 , 127-137 | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
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Cross-contrast image translation is an important task for completing missing contrasts in clinical diagnosis. However, most existing methods learn separate translator for each pair of contrasts, which is inefficient due to many possible contrast pairs in real scenarios. In this work, we propose a unified Hyper-GAN model for effectively and efficiently translating between different contrast pairs. Hyper-GAN consists of a pair of hyper-encoder and hyper-decoder to first map from the source contrast to a common feature space, and then further map to the target contrast image. To facilitate the translation between different contrast pairs, contrast-modulators are designed to tune the hyper-encoder and hyper-decoder adaptive to different contrasts. We also design a common space loss to enforce that multi-contrast images of a subject share a common feature space, implicitly modeling the shared underlying anatomical structures. Experiments on two datasets of IXI and BraTS 2019 show that our Hyper-GAN achieves state-of-the-art results in both accuracy and efficiency, e.g., improving more than 1.47 and 1.09 dB in PSNR on two datasets with less than half the amount of parameters.

Keyword :

Multi-contrast MR Unpaired image translation Unified hyper-GAN

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GB/T 7714 Yang, Heran , Sun, Jian , Yang, Liwei et al. A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation [C] . 2021 : 127-137 .
MLA Yang, Heran et al. "A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation" . (2021) : 127-137 .
APA Yang, Heran , Sun, Jian , Yang, Liwei , Xu, Zongben . A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation . (2021) : 127-137 .
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SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning EI SCIE
期刊论文 | 2021 , 51 (3) , 1556-1570 | IEEE Transactions on Cybernetics
WoS CC Cited Count: 5
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Abstract :

It is known that boosting can be interpreted as an optimization technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which proves to be very sensitive to random noise/outliers. Therefore, several boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this article, we present a new way to robustify AdaBoost, that is, incorporating the robust learning idea of self-paced learning (SPL) into the boosting framework. Specifically, we design a new robust boosting algorithm based on the SPL regime, that is, SPLBoost, which can be easily implemented by slightly modifying off-the-shelf boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost. © 2013 IEEE.

Keyword :

Robustness (control systems) Adaptive boosting

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GB/T 7714 Wang, Kaidong , Wang, Yao , Zhao, Qian et al. SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning [J]. | IEEE Transactions on Cybernetics , 2021 , 51 (3) : 1556-1570 .
MLA Wang, Kaidong et al. "SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning" . | IEEE Transactions on Cybernetics 51 . 3 (2021) : 1556-1570 .
APA Wang, Kaidong , Wang, Yao , Zhao, Qian , Meng, Deyu , Liao, Xiuwu , Xu, Zongben . SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning . | IEEE Transactions on Cybernetics , 2021 , 51 (3) , 1556-1570 .
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Variational bayes' method for functions with applications to some inverse problems EI SCIE
期刊论文 | 2021 , 43 (1) , A355-A383 | SIAM Journal on Scientific Computing
WoS CC Cited Count: 1
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The Bayesian approach, a useful tool for quantifying uncertainties, has been extensively employed to solve the inverse problems of partial differential equations (PDEs). One of the main difficulties in employing the Bayesian approach to such problems is how to extract information from the posterior probability measure. Compared with conventional sampling-type methods, the variational Bayes method (VBM) has been intensively examined in the field of machine learning attributed to its ability in extracting approximately the posterior information with lower computational cost. In this paper, we generalize the conventional finite-dimensional VBM to the infinitedimensional space to rigorously solve the inverse problems of PDEs. We further establish a general infinite-dimensional mean-field approximate theory and apply it to the linear inverse problems under the Gaussian and Laplace noise assumptions at the abstract level. The results of some numerical experiments substantiate the effectiveness of the proposed approach. © 2021 Society for Industrial and Applied Mathematics.

Keyword :

Bayesian networks Differential equations Computation theory Inverse problems

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GB/T 7714 JIA, JUNXIONG , ZHAO, QIAN , XU, ZONGBEN et al. Variational bayes' method for functions with applications to some inverse problems [J]. | SIAM Journal on Scientific Computing , 2021 , 43 (1) : A355-A383 .
MLA JIA, JUNXIONG et al. "Variational bayes' method for functions with applications to some inverse problems" . | SIAM Journal on Scientific Computing 43 . 1 (2021) : A355-A383 .
APA JIA, JUNXIONG , ZHAO, QIAN , XU, ZONGBEN , MENG, DEYU , LEUNG, YEE . Variational bayes' method for functions with applications to some inverse problems . | SIAM Journal on Scientific Computing , 2021 , 43 (1) , A355-A383 .
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