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Multiscale sparse Dictionary Learning With Rate Constraint for Seismic Data Compression

IEEE ACCESS 2019年 7卷 86651-86663页

作者： Tian, Xin Wuhan Univ Elect Informat Sch Wuhan 430072 Hubei Peoples R China

Seismic survey is one of the most effective tools for oil and gas exploration. To date, there has been an exponential growth in the size of seismic data required for large-scale seismic survey. For transmission and storage purposes, we propose a novel seismic compression method. First, a multiscale sparse dictionary learning model with rate constraint is presented. By combining the advantages of multiscale decomposition and dictionary learning, the seismic data could be effectively represented as a sparse matrix. Rate constraints are used to obtain the sparse coefficients that are properly tailored to the compression objective. To solve the optimization problem, the alternating direction method of multipliers is adopted. Furthermore, a seismic compression scheme based on the learned dictionary is introduced. Finally, public seismic datasets are used to verify the efficiency of different seismic data compression methods. The experimental results indicate that the proposed method achieves the best seismic compression performance, including rate-distortion tradeoff and visual quality.

关键词： Multiscale dictionary learning rate constraint sparse coding seismic data compression

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Leakage Identification in Water Distribution Networks with Error Tolerance Capability

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WATER RESOURCES MANAGEMENT 2019年第3期33卷 1233-1247页

作者： Xie, Xiang Hou, Dibo Tang, Xiaoyu Zhang, Hongjian Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Zhejiang Peoples R China

Leakages in water distribution networks have caused considerable waste of water resources. Thus, this study proposes a novel method for hydraulically monitoring and identifying regions where leakages occur in near-real time. A large network is first divided into several identification regions. To exploit a strong constructive and discriminative power, sparse coding is used, thereby adaptively coding the information embedded in observed pressures efficiently and succinctly. And a linear classifier is trained to determine the most likely leakage regions. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. Results indicate that the proposed method can identify leakage events with enhanced tolerance capability for measurement errors. The method is also partially effective for identifying two simultaneous leakages. Certain practical advice in balancing the number of sensors and regions is also discussed to enhance the application potential of this method.

关键词： Water distribution network Leakage identification Pressure residual sparse coding Dictionary learning

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Exploiting Sparsity Recovery for Compressive Spectrum Sensing: A Machine Learning Approach

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IEEE ACCESS 2019年 7卷 126098-126110页

作者： Nazzal, Mahmoud Ekti, Ali Riza Gorcin, Ali Arslan, Huseyin Istanbul Medipol Univ Sch Engn & Nat Sci TR-34810 Istanbul Turkey Balikesir Univ Dept Elect & Elect Engn TR-10100 Balikesir Turkey Yildiz Tech Univ Fac Elect & Commun Engn TR-34220 Istanbul Turkey TUBITAK Informat & Informat Secur Res Ctr BILGEM TR-41470 Gebze Turkey Univ S Florida Dept Elect Engn Tampa FL 33620 USA

Sub-Nyquist sampling for spectrum sensing has the advantages of reducing the sampling and computational complexity burdens. However, determining the sparsity of the underlying spectrum is still a challenging issue for this approach. Along this line, this paper proposes an algorithm for narrowband spectrum sensing based on tracking the convergence patterns in sparse coding of compressed received signals. First, a compressed version of a received signal at the location of interest is obtained according to the principle of compressive sensing. Then, the signal is reconstructed via sparse recovery over a learned dictionary. While performing sparse recovery, we calculate the sparse coding convergence rate in terms of the decay rate of the energy of residual vectors. Such a decay rate is conveniently quantified in terms of the gradient operator. This means that while compressive sensing allows for sub-Nyquist sampling thereby reducing the analog-to-digital conversion overhead, the sparse recovery process could be effectively exploited to reveal spectrum occupancy. Furthermore, as an extension to this approach, we consider feeding the energy decay gradient vectors as features for a machine learning-based classification process. This classification further enhances the performance of the proposed algorithm. The proposed algorithm is shown to have excellent performances in terms of the probability-of-detection and false-alarm-rate measures. This result is validated through numerical experiments conducted over synthetic data as well as real-life measurements of received signals. Moreover, we show that the proposed algorithm has a tractable computational complexity, allowing for real-time operation.

关键词： sparse coding spectrum sensing machine learning classification residual components

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PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems

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SIGNAL PROCESSING 2019年 162卷 83-96页

作者： Shi, Baoshun Lian, Qiusheng Fan, Xiaoyu Yanshan Univ Sch Informat Sci & Engn Qinhuang Dao 066004 Hebei Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuang Dao 066004 Hebei Peoples R China

The problem of recovering an image of interest from nonlinear measured data is challenging. To address this nonlinear imaging inverse problem, we propose a novel Plug-and-Play Regularization (PPR) approach that can exploit multiple priors. The underlying image and its filtered image by a denoiser should have similar structures. To exploit this similarity, we enforce the similarity of their sparse coefficients with respect to a tight frame. We formulate a PPR-based nonlinear imaging optimization problem and solve it by using the alternating optimization strategy that consists of filtering step, sparse coding step and image updating step. To avoid the finely-tuned regularization parameter, the epigraph concept is employed in the image updating step. Multiple priors, including the priors employed by the denoiser and the sparsity in the sparsifying transform domain, can be utilized in the proposed PPR model. Under the coded diffraction imaging scenario, we show that the proposed algorithm of exploiting a deep denoiser can achieve higher quality images, compared to the previous imaging algorithms. (C) 2019 Published by Elsevier B.V.

关键词： Nonlinear imaging inverse problem Computational imaging Plug-and-play priors Deep denoiser sparse coding

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Robust reservoir rock fracture recognition based on a new sparse feature learning and data training method

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2019年第4期30卷 2113-2146页

作者： Taibi, Fatemeh Akbarizadeh, Gholamreza Farshidi, Ebrahim Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Ahvaz Iran

In this paper, the main goal is to identify the sine fractures of reservoir rock automatically. Therefore, a five-step algorithm is applied on the imaging logs. The first step consists of extracting the features of the imaging log by applying the Zernike moments. In the second step, the features are learned by using sparse coding. In the third step, the imaging log is segmented by using the self-organizing map neural network and the training dataset. In the fourth step, the fracture points are extracted by Steger method. In the last step, to determine the sine parameters of fractures, the Hough transform is applied to the image fracture points. The experimental results show that the proposed algorithm is highly able to detect the fractures of the imaging logs successfully. Also, the precision of the proposed method to extract the fracture pixels is so high and it has low sensitivity to noise in the imaging logs. In this paper, the proposed algorithm has been applied on the imaging datasets of FMI and the obtained results show that the classification has better precision compared with other proposed algorithm.

关键词： Imaging log Zernike moments sparse coding Self-organizing neural network Steger method Hough transform

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sparse Representation in Awake Auditory Cortex: Cell-type Dependence, Synaptic Mechanisms, Developmental Emergence, and Modulation

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CEREBRAL CORTEX 2019年第9期29卷 3796-3812页

作者： Liang, Feixue Li, Haifu Chou, Xiao-lin Zhou, Mu Zhang, Nicole K. Xiao, Zhongju Zhang, Ke K. Tao, Huizhong W. Zhang, Li I. Southern Med Univ Sch Biomed Engn Dept Med Engn Guangzhou 510515 Guangdong Peoples R China Univ Southern Calif Keck Sch Med Zilkha Neurogenet Inst Los Angeles CA 90033 USA Univ Southern Calif Keck Sch Med Dept Physiol & Neurosci Los Angeles CA 90033 USA Univ Southern Calif Neurosci Grad Program Los Angeles CA 90089 USA Southern Med Univ Sch Basic Med Sci Dept Physiol Guangzhou 510515 Guangdong Peoples R China Univ North Dakota Dept Pathol Grand Forks ND 58202 USA

sparse representation is considered an important coding strategy for cortical processing in various sensory modalities. It remains unclear how cortical sparseness arises and is being regulated. Here, unbiased recordings from primary auditory cortex of awake adult mice revealed salient sparseness in layer (L)2/3, with a majority of excitatory neurons exhibiting no increased spiking in response to each of sound types tested. sparse representation was not observed in parvalbumin (PV) inhibitory neurons. The nonresponding neurons did receive auditory-evoked synaptic inputs, marked by weaker excitation and lower excitation/inhibition (E/I) ratios than responding cells. sparse representation arises during development in an experience-dependent manner, accompanied by differential changes of excitatory input strength and a transition from unimodal to bimodal distribution of E/I ratios. sparseness level could be reduced by suppressing PV or L1 inhibitory neurons. Thus, sparse representation may be dynamically regulated via modulating E/I balance, optimizing cortical representation of the external sensory world.

关键词： cell attached recording cortical inhibitory neuron excitation/inhibition balance developmental plasticity Silent neuron sparse coding

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REMOVAL OF THE TWIN IMAGE ARTIFACT IN HOLOGRAPHIC LENS-FREE IMAGING BY sparse DICTIONARY LEARNING AND coding 14

REMOVAL OF THE TWIN IMAGE ARTIFACT IN HOLOGRAPHIC LENS-FREE ...

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Haeffele, Benjamin D. Roth, Sophie Zhou, Lin Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD USA IMEC Leuven Belgium

ISBN: (纸本)9781509011728

Mitigating the effects of the twin image artifact is one of the key challenges in holographic lens-free microscopy. This artifact arises due to the fact that imaging detectors can only record the magnitude of the hologram wavefront but not the phase. Prior work addresses this problem by attempting to simultaneously estimate the missing phase and reconstruct an image of the object specimen. Here we explore a fundamentally different approach based on post-processing the reconstructed image using sparse dictionary learning and coding techniques originally developed for processing conventional images. First, a dictionary of atoms representing characteristics from either the true image of the specimen or the twin image are learned from a collection of patches of the observed images. Then, by expressing each patch of the observed image as a sparse linear combination of the dictionary atoms, the observed image is decomposed into a component that corresponds to the true image and another one that corresponds to the twin image artifact. Experiments on counting red blood cells demonstrate the effectiveness of the proposed approach.

关键词： Holography lens-free imaging sparse coding dictionary learning blood cell counting

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Descriptor extraction based on a multilayer dictionary architecture for classification of natural images

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COMPUTER VISION AND IMAGE UNDERSTANDING 2020年 191卷 102708-000页

作者： Tim, Stefen Chan Wai Rombaut, Michele Pellerin, Denis Dutt, Anuvabh Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France Univ Grenoble Alpes CNRS Grenoble INP LIG F-38000 Grenoble France

This paper presents a descriptor extraction method in the context of image classification, based on a multilayer structure of dictionaries. We propose to learn an architecture of discriminative dictionaries for classification in a supervised framework using a patch-level approach. This method combines many layers of sparse coding and pooling in order to reduce the dimension of the problem. The supervised learning of dictionary atoms allows them to be specialized for a classification task. The method has been tested on known datasets of natural images such as MNIST, CIFAR-10 and STL, in various conditions, especially when the size of the training set is limited, and in a transfer learning application. The results are also compared with those obtained with Convolutional Neural Network (CNN) of similar complexity in terms of number of layers and processing pipeline.

关键词： Supervised dictionary learning sparse coding Classification

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Robust navigation support in lowest dose image setting

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第2期14卷 291-300页

作者： Mai Bui Bourier, Felix Baur, Christoph Milletari, Fausto Navab, Nassir Demirci, Stefanie Tech Univ Munich Comp Aided Med Procedures Boltzmannstr 3 D-85748 Garching Germany Deutsch Herzzentrum Munich Lazarettstr 5 D-81241 Munich Germany

PurposeClinical cardiac electrophysiology (EP) is concerned with diagnosis and treatment of cardiac arrhythmia describing abnormality or perturbation in the normal activation sequence of the myocardium. With the recent introduction of lowest dose X-ray imaging protocol for EP procedures, interventional image enhancement has gained crucial importance for the well-being of patients as well as medical *** this paper, we introduce a novel method to detect and track different EP catheter electrodes in lowest dose fluoroscopic sequences based on 1-sparse coding and online robust PCA (ORPCA). Besides being able to work on real lowest dose sequences, the underlying methodology achieves simultaneous detection and tracking of three main EP catheters used during ablation *** have validated our algorithm on 16 lowest dose fluoroscopic sequences acquired during real cardiac ablation procedures. In addition to expert labels for 2 sequences, we have employed a crowdsourcing strategy to obtain ground truth labels for the remaining 14 sequences. In order to validate the effect of different training data, we have employed a leave-one-out cross-validation scheme yielding an average detection rate of 86.9%.ConclusionBesides these promising quantitative results, our medical partners also expressed their high satisfaction. Being based on 1-sparse coding and online robust PCA (ORPCA), our method advances previous approaches by being able to detect and track electrodes attached to multiple different catheters.

关键词： Computer-assisted electrophysiology Electrode tracking sparse coding Online robust PCA Online tracking Fluoroscopy

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Online dictionary learning method for extracting GPR diffractions

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2019年第6期16卷 1116-1123页

作者： Zhao, Jingtao Yu, Caixia Peng, Suping Chen, Zongnan State Key Lab Water Resource Protect & Utilizat C Beijing 100011 Peoples R China China Univ Min & Technol Beijing Beijing 100083 Peoples R China Chinese Acad Sci Inst Geol & Geophys Key Lab Petr Resources Res Beijing 100029 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China

Diffractions in a Ground-Penetrating Radar (GPR) data carry significant responses from near-surface small-scale fractures or karsts. However, this geological information is generally difficult to extract because of the shielding effect of strong reflections from subsurface layers. In order to solve this problem, a GPR diffraction extraction method is proposed for individually separating and imaging of GPR diffractions that incorporates a local plane-wave destruction filter with an online dictionary learning algorithm. The strong reflections are estimated and eliminated by the local plane-wave destruction method and the weak GPR diffractions are extracted by a sparse coding algorithm. In solving this model, a trust-region algorithm is used for accelerating the sparse coding procedures that can scale up gracefully to a large GPR data processing. A numerical experiment demonstrates the good performance of the proposed method in destroying strong reflections and enhancing weak diffractions from small-scale void holes. Real data application further verifies its potential value in resolving fine details of subsurface small-scale buried targets, such as pipes or void holes.

关键词： GPR diffraction extraction dictionary learning sparse coding

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