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Multi-frame super-resolution reconstruction based on mixed Poisson Gaussian noise

SIGNAL processing-image COMMUNICATION 2020年第0期82卷 115736-000页

作者： Sun, Kaicong Trung-Hieu Tran Krawtschenko, Roman Simon, Sven Univ Str 38 D-70569 Stuttgart Germany

In this paper, a non-blind multi-frame super-resolution (SR) model based on mixed Poisson Gaussian noise (MPGSR) is proposed. Poisson noise arises from the stochastic nature of the photon-counting process. Readout noise and reset noise inherent to the readout circuitry can be modeled by an additive Gaussian noise. Therefore, a mixed Poisson-Gaussian noise model is more appropriate for real imaging system. Instead of deriving the data fidelity term from the perspective of error norms and the corresponding influence functions, we address the multi-frame SR problem based on a statistical noise model. The derived objective function is decomposed into sub-functions and solved by the alternating direction method of multipliers (ADMM) algorithm which allows using techniques of constrained optimization. The validation of the proposed MPGSR was performed quantitatively and qualitatively on natural and X-ray images. In comparison to the optimization-based and learning-based state-of-the-art methods, we have demonstrated the feasibility of MPGSR and the significance of applying a more appropriate noise model on the SR image reconstruction.

关键词： Super-resolution Mixed Poisson Gaussian noise statistical image reconstruction

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Creation of Scenarios for processing Multidimensional Spatial-Temporal Data under Conditions of Uncertainty 22

Creation of Scenarios for Processing Multidimensional Spatia...

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22nd International Conference on Soft Computing and Measurements (SCM)

作者： Pisarev, Ivan A. Kotova, Elena E. Pisarev, Andrei S. St Petersburg Electrotech Univ LETI Fac Comp Sci & Technol St Petersburg Russia

ISBN: (纸本)9781728136028

The article discusses a method for constructing scenarios in an adaptive analytical information system for classifying objects and their movement trajectories in real time based on processing information from a large number of spatially separated information sources (sensors) and analyzing dynamic arrays of distributed multidimensional data under conditions of uncertainty. Examples of scenarios that use statistical, interval, fuzzy and stochastic methods for describing uncertainty are given. When developing scenarios for adaptive data processing and analysis, integrated ontologies of the "hydroacoustics" field of knowledge, ontologies of signal processing methods, image processing methods and data analysis methods are used. Creation of scenarios for automated processing of multidimensional spatial-temporal data is carried out in the OntoMASTER network software environment based on semantic Web standards. To support scientific research, a dynamic scripting interface and their testing in a simulator environment is implemented. The learning environment based on the knowledge base is intended for training and retraining of personnel. The results of the work can be applied both in scientific research and in the process of teaching students.

关键词： information processing building scenarios uncertainty conditions probabilistic methods classification

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stochastic deterministic methods for processing signals and images in optical electronic systems

Stochastic deterministic methods for processing signals and ...

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44th WILGA Symposium on Photonics Applications and Web Engineering

作者： Strikova, Tetiana O. Lytyuga, Oleksandr P. Skorupski, Krzysztof Bugubayeva, Alina Kharkiv Natl Univ Radio Elect Kharkiv Ukraine Lublin Univ Technol Nadbystrzycka 38A PL-20618 Lublin Poland East Kazakhstan State Tech Univ AK Protozanov St 69 Ust Kamenogorsk 070004 Kazakhstan

ISBN: (纸本)9781510630666

The theoretical and experimental studies of statistical properties of the input signals of optical electronic systems were carried out. The statistical models of interaction of an optical signal in optoelectronic systems based on the the distribution laws, characterized by a finite and infinite dispersion, were developed. The study of the asymptotic behavior of tailings of the output signals distribution densities for optoelectronic systems proved the possibility of using stable distribution laws describing the source signals of optoelectronic systems. The boundaries of statistical models applicability were outlined based on the central and generalized boundary theorems. Theoretical and experimental researches with the provision of high accuracy of determination of signals spatial-temporal characteristics with increased probabilistic detection characteristics were presented.

关键词： Optoelectronic systems photodetector signal fluctuations the Poisson model of signals the Gaussian and non-Gaussian statistics generalized limit theorems signal detection image processing methods

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Towards a unified quadrature framework for large-scale kernel machines

arXiv

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arXiv 2020年

作者： Liu, Fanghui Huang, Xiaolin Chen, Yudong Suykens, Johan A.K. KU Leuven LeuvenB-3001 Belgium Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China School of Operations Research and Information Engineering Cornell University IthacaNY14850 United States

In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation. Considering that the integration domain and measure of typical kernels, e.g., Gaussian kernels, arc-cosine kernels, are fully symmetric, we leverage deterministic fully symmetric interpolatory rules to efficiently compute quadrature nodes and associated weights for kernel approximation. The developed interpolatory rules are able to reduce the number of needed nodes while retaining a high approximation accuracy. Further, we randomize the above deterministic rules by the classical Monte-Carlo sampling and control variates techniques with two merits: 1) The proposed stochastic rules make the dimension of the feature mapping flexibly varying, such that we can control the discrepancy between the original and approximate kernels by tuning the dimnension. 2) Our stochastic rules have nice statistical properties of unbiasedness and variance reduction with fast convergence rate. In addition, we elucidate the relationship between our deterministic/stochastic interpolatory rules and current quadrature rules for kernel approximation, including the sparse grids quadrature and stochastic spherical-radial rules, thereby unifying these methods under our framework. Experimental results on several benchmark datasets show that our methods compare favorably with other representative kernel approximation based methods. Copyright © 2020, The Authors. All rights reserved.

关键词： stochastic systems

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Mapping stochastic Collective Behavior Distinguishes Subtle Mutations in Social Bacteria

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PRX Life 2023年第2期1卷 023011-023011页

作者： Merrill E. Asp Eduardo A. Caro Roy D. Welch Alison E. Patteson BioInspired Institute Syracuse University Syracuse New York 13244 USA Physics Department Syracuse University Syracuse New York 13244 USA Biology Department Syracuse University Syracuse New York 13244 USA

The genotype-to-phenotype problem (G2P) for multicellular development asks how genetic inputs control collective phenotypic outputs. However, this is a challenging problem due to gene redundancy and stochasticity, causing mutations to have subtle phenotypic effects and replicates to display significant variation. We approach this problem using the model organism Myxococcus xanthus, a motile self-organizing bacterium that forms three-dimensional cell aggregates that mature into spore-filled fruiting bodies when under starvation stress. We develop a high-throughput imaging method using three-dimensional-printed microscopes to efficiently collect large phenotypic datasets. Our automated methods for analysis and visualization produce a map of phenotypic variation in M. xanthus development. We demonstrate that even subtle effects on developmental dynamics caused by mutation can be identified, discriminated, characterized, and given statistical significance, with implications for future gene annotation studies and the effect of environmental factors on G2P.

关键词： Developmental biology image processing Bacteria Biofilms

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A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks 10

A case study on homogeneous and heterogeneous reservoir poro...

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10th International Conference on Applied Energy (ICAE)

作者： Liu, Siyan Zhong, Zhi Takbiri-Borujeni, Ali Kazemi, Mohammad Fu, Qinwen Yang, Yuhao Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA Univ Texas Austin Bur Econ Geol Austin TX 78713 USA West Virginia Univ Dept Petr & Nat Gas Engn Morgantown WV 26506 USA Slippery Rock Univ Dept Petr & Nat Gas Engn Slippery Rock PA 16057 USA

The advancing of modern X-ray computer tomography technology provides a powerful tool for us to illustrate the details inside the reservoir rock in three-dimensional space. Pore-scale rock characterization, modeling, and related fluid flow simulation can be challenging due to the high complexity of various rock samples. Conventional pore scale structure modeling methods such as various stochastic methods were developed for reservoir rock 3D microscopic structure reconstruction in order to generate representative realizations for numerical simulations and property upscaling approaches. In this work, generative adversarial networks (GANs) is used for generating the synthetic micro representations of porous rock by acquiring non-linear statistical information from the real 3D rock images in an unsupervised learning scheme. The related 3D image pre-processing, network training and adjusting as well as data post-processing procedures are addressed. The network prediction results from a homogeneous Berea sandstone and a heterogeneous Estaillades carbonate demonstrated the capability of GANs for high resolution porous rock image representations reconstruction, generated and real images are compared via various visualizations and inspections. The study also illustrated the importance of the training image preprocessing, which indicating the data augmentation techniques can be one of the promising improvements in terms of capturing the sparsely distributed features from heterogenous 3D images and reconstructing the synthetic realizations, meanwhile, the robustness of the model during training process is enhanced when limited real data is available. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： porous media image reconstruction generative adversarial networks neural networks

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Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

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ADVANCES IN SPACE RESEARCH 2018年第6期61卷 1558-1572页

作者： Ranaie, Mehrdad Soffianian, Alireza Pourmanafi, Saeid Mirghaffari, Noorollah Tarkesh, Mostafa Isfahan Univ Technol Dept Nat Resources Esfahan *** Iran

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Non-parametric image classification Neural network with feature extraction Bagged CART Random forest stochastic gradient boosting Worldview-3

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MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS 25

MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS

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25th IEEE International Conference on image processing (ICIP)

作者： Vidal, Ana Fernandez Pereyra, Marcelo Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9781479970612

This paper presents an empirical Bayesian method to estimate regularisation parameters in imaging inverse problems. The method calibrates regularisation parameters directly from the observed data by maximum marginal likelihood estimation, and is useful for inverse problems that are convex. A main novelty is that maximum likelihood estimation is performed efficiently by using a stochastic proximal gradient algorithm that is driven by two proximal Markov chain Monte Carlo samplers, intimately combining modern optimisation and sampling techniques. The proposed methodology is illustrated with an application to total-variation image deconvolution, where it compares favourably to alternative Bayesian and non-Bayesian approaches from the state of the art.

关键词： image processing inverse problems statistical inference empirical Bayes stochastic optimisation Markov chain Monte Carlo methods proximal algorithms

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Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances

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SIGNAL processing 2018年 144卷 68-76页

作者： Bindilatti, Andre A. Vieira, Marcelo A. C. Mascarenhas, Nelson D. A. Univ Fed Sao Carlos Comp Dept Sao Paulo Brazil Univ Sao Paulo Dept Elect & Comp Engn Sao Carlos SP Brazil Fac Campo Limpo Paulista Campo Limpo Paulista SP Brazil

The Wiener filter is a classical approach for signal denoising and it is known to be the optimum filter in the sense of the linear minimum mean square error (LMMSE). In general, knowledge of some statistical parameters of the underlying signal and noise are required to compute the Wiener filter. These parameters are usually estimated from the corrupted image using a local neighborhood, thus assuming that the signal and noise are locally stationary. However, images corrupted by signal-dependent noise, such as Poisson noise, are not locally stationary. In this paper, we are proposing a novel Wiener filtering approach for Poisson corrupted images considering a non-local weighted parameter estimation. In the proposed method, named Poisson Non-Local Wiener filter (PNL-Wiener), filtering parameters are estimated from the degraded image considering a non-local neighborhood, where the weights of the estimation function are computed based on the stochastic distances between image patches. Experimental results show that the proposed method is competitive to other state-of-the-art denoising methods designed specifically for Poisson corrupted images, yet providing better preservation of edges and fine details in the images. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Wiener filter Poisson noise Non-local means image denoising

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基于无人机倾斜摄影的露头多点地质统计模拟——以山西吕梁坪头乡石盒子组为例

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石油学报 2021年第2期42卷 198-216页

作者：印森林高阳胡张明熊亭冯文杰赵俊威程乐利长江大学录井技术与工程研究院湖北荆州434023 长江大学地球科学学院湖北武汉430100 中国石油新疆油田公司勘探开发研究院新疆克拉玛依834000 中国石油天然气西部钻探工程公司新疆克拉玛依834000

针对传统野外露头研究方法的不足,利用无人机倾斜摄影具有敏捷性、视角全局性及定量化程度高的特点,对野外三角洲露头数据进行了采集、处理与地质解释,建立了三维数字化露头模型和定量储层地质知识库;结合多露头剖面精细解剖及对比研究... 详细信息

针对传统野外露头研究方法的不足,利用无人机倾斜摄影具有敏捷性、视角全局性及定量化程度高的特点,对野外三角洲露头数据进行了采集、处理与地质解释,建立了三维数字化露头模型和定量储层地质知识库;结合多露头剖面精细解剖及对比研究,采用岩相组合、层次结构、平剖互动、序贯指示随机模拟和多点地质统计模拟等方法,建立了露头区三维多点地质统计模型。研究结果表明:(1)无人机采集的倾斜摄影数据体在处理后的三维坐标体系与拍摄的高精度图像信息全面耦合,实现了野外露头地质信息三维数字化、可视化和定量化。在井点设置下,结合单井岩相约束,开展井间基准面旋回露头对比与小层追踪,建立了露头区空间地层框架和定量储层地质知识库。(2)将传统野外露头剖面解剖与现代无人机倾斜摄影定量储层地质知识库结合起来研究,是建立研究区地质属性空间分布和地质模式的互补手段,可以增强研究结论的合理性。露头区自下而上发育了构型差异较大的3类成因砂体,即交叉叠置型分流河道、分流河道与席状砂坝叠置型和薄层席状砂型或孤立分流河道型等骨架砂体类型。(3)无人机倾斜摄影模型将二维非规则露头剖面研究向三维空间拓展,建立了三维序贯指示随机模拟模型,不仅加强了剖面间的关联性,还为露头平面沉积微相研究、平剖互动精细刻画砂体构型分布提供了强大支撑。(4)基于平剖互动及定量储层地质知识库数据确定了三维训练图像结构模式,最后开展了三维多点地质统计模拟,并探讨了相控孔隙度参数及"甜点"分布规律。研究结果显示,多点地质统计模型的实现、各层砂体构型分布与定量储层地质知识库差异较小;同时,各种模型统计数据值差异较小,多点三维模拟实现的模型可靠程度高。

关键词：无人机倾斜摄影数字露头三维露头建模多点地质统计学训练图像三角洲储层构型坪头乡

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