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image Deno sing via Multiscale Nonlinear Diffusion Models

SIAM JOURNAL ON IMAGING SCIENCES 2017年第3期10卷 1234-1257页

作者： Feng, Wensen Qiao, Peng Xi, Xuanyang Chen, Yunjin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Natl Univ Def Technol Sch Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China ULSee Inc Hangzhou 310016 Zhejiang Peoples R China

image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers are dedicated to image denoising. In the past decade, state-of-the-art denoising algorithms have been clearly dominated by nonlocal patch-based methods, which explicitly exploit patch self-similarity within the targeted image. However, in the past two years, discriminatively trained local approaches have started to outperform previous nonlocal models and have been attracting increasing attention due to the additional advantage of computational efficiency. Successful approaches include cascade of shrinkage fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two methods are built on the filter response of linear filters of small size using feed forward architectures. Due to the locality inherent in local approaches, the CSF and TNRD models become less effective when the noise level is high and consequently introduce some noise artifacts. In order to overcome this problem, in this paper we introduce a multiscale strategy. To be specific, we build on our newly developed TNRD model, adopting the multiscale pyramid image representation to devise a multiscale nonlinear diffusion process. As expected, all the parameters in the proposed multiscale diffusion model, including the filters and the influence functions across scales, are learned from training data through a loss-based approach. Numerical results on Gaussian and Poisson denoising substantiate that the exploited multiscale strategy can successfully boost the performance of the original TNRD model with a single scale. As a consequence, the resulting multiscale diffusion models can significantly suppress the typical incorrect features for those noisy images with heavy noise. It turns out that multiscale TNRD variants achieve better performance than state-of-the-art denoising methods.

关键词： image denoising multiscale pyramid image representation trainable nonlinear reaction diffusion model Gaussian denoising Poisson denoising

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parallelization of Synthetic Aperture Radar (SAR) image Formation Algorithm 1st

Parallelization of Synthetic Aperture Radar (SAR) Image Form...

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1st International Conference on Computational Intelligence and Informatics (ICCII)

作者： Rao, P. V. R. R. Bhogendra Shashank, S. S. Def Res & Dev Lab Hyderabad Andhra Pradesh India BrahMos Aerosp Private Ltd Hyderabad Andhra Pradesh India

ISBN: (纸本)9789811024719;9789811024702

Synthetic aperture radar (SAR)-based platforms have to process increasingly large number of complex floating-point operations and have to meet hard real-time deadlines. However, real-time use of SAR is severely restricted by computation time taken for image formation. One of the classical methods of reducing this computation time to make it suitable for real-time application is multi-processing. A successful attempt has been made by the authors to develop and test a parallel algorithm for synthetic aperture radar image formation, and the results are presented in this paper.

关键词： Synthetic aperture radar SAR image formation Digital signal processing parallel computing distributed computing

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A Novel Approach for Identifying Lateral Movement Attacks Based on Network Embedding

A Novel Approach for Identifying Lateral Movement Attacks Ba...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Mingyi Chen Yepeng Yao Junrong Liu Bo Jiang Liya Su Zhigang Lu School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728111421;9781728111414

The growing targeted incidents constitute a permanent menace to internal security with more frequent data breach and service interruption events nowadays. Attackers move laterally and reside in the internal systems for accessing valuable information continuously. This paper proposed a novel approach based on network embedding synthesizing the information on hosts, traffics and correlations to forestall further loss under a deliberate attack. The approach begins with constructing a host communication graph with features extracted from the original data recorded in the internal network and the topology structures of the constructed network. Inspired by previous works, the approach uses a features aggregation learning method, that is composing the features on vertices and edges with neighbors' features to aggregate new composite features. Then the features are selected and learned iteratively. The ultimately selected features were reduced to lower dimension for training and used for the malicious host classification task. Besides, the approach can reemployment the classification results to optimize the dimensionality reduction. Compared with the state-of-the-art models and methods, the proposed approach is (i) flexible with multiple types of data and exchangeable methods, (ii) accurate in feature learning, selecting and extracting with the remarkably average accuracy of 99.9% and the average precision of 91.3%.

关键词： Feature extraction Topology Training Biological system modeling image edge detection Security Data mining

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Study about the Edge Detection Algorithm and its Applications 18

Study about the Edge Detection Algorithm and its Application...

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18th International Carpathian Control Conference (ICCC)

作者： Popa, Bogdan Univ Craiova Dept Automat & Appl Informat Craiova Romania

ISBN: (纸本)9781509048625

Nowadays one of the most used algorithm for the image processing systems is the edge detection algorithm, starting from the identification systems in different domains to the complex systems direct from industry. With more different approaches this algorithm is today necessary in priority areas and his performance of time processing, quality differ in each case. In the backside of the algorithm there are a lot of mathematical calculus that influence the speed and the accuracy of the results. This article highlights some methods that compose this algorithm and propose some ideas and some tests over the possibility of distributed calculus that influences his performance. In this study it is proposed a set of analysis over the results over more tests and case scenarios that show them main problems in the calculus part. Also a parallel implementation that can offer a better perspective of this algorithm is proposed. Starting with this point over the edge detection of the algorithm, in this paper it is revealed also a good utilization in another different domain for this algorithm.

关键词： image processing edge detection algorithm performance case study

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Cloud masking and removal in remote sensing image time series

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Gomez-Chova, Luis Amoros-Lopez, Julia Mateo-Garcia, Gonzalo Munoz-Mari, Jordi Camps-Valls, Gustau Univ Valencia Image Proc Lab Catedratico Jose Beltran 2 E-46980 Valencia Spain

Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clouds. The method estimates the background surface changes using the information in the time series. In particular, we propose linear and nonlinear least squares regression algorithms that minimize both the prediction and the estimation error simultaneously. Then, significant differences in the image of interest with respect to the estimated background are identified as clouds. The use of kernel methods allows the generalization of the algorithm to account for higher-order (nonlinear) feature relations. After the proposed cloud masking and cloud removal, cloud-free time series at high spatial resolution can be used to obtain a better monitoring of land cover dynamics and to generate more elaborated products. The method is tested in a dataset with 5-day revisit time series from SPOT-4 at high resolution and with Landsat-8 time series. Experimental results show that the proposed method yields more accurate cloud masks when confronted with state-of-the-art approaches typically used in operational settings. In addition, the algorithm has been implemented in the Google Earth Engine platform, which allows us to access the full Landsat-8 catalog and work in a parallel distributed platform to extend its applicability to a global planetary scale. (C) 2017 Society of Photo-Optical Instrumenta

关键词： image time series multitemporal analysis change detection cloud masking kernel ridge regression Google Earth Engine

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image Annotation by Deep Neural Networks with Attention Shaping 9

Image Annotation by Deep Neural Networks with Attention Shap...

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9th International Conference on Digital image processing (ICDIP)

作者： Zheng, Kexin Lv, Shaohe Ma, Fang Chen, Fei Jin, Chi Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc 109 Deya Rd Changsha 410073 Hunan Peoples R China Univ South China Sch Comp Sci & Technol Changsha 410012 Hunan Peoples R China

ISBN: (纸本)9781510613058;9781510613041

image annotation is a task of assigning semantic labels to an image. Recently, deep neural networks with visual attention have been utilized successfully in many computer vision tasks. In this paper, we show that conventional attention mechanism is easily misled by the salient class, i.e., the attended region always contains part of the image area describing the content of salient class at different attention iterations. To this end, we propose a novel attention shaping mechanism, which aims to maximize the non-overlapping area between consecutive attention processes by taking into account the history of previous attention vectors. Several weighting polices are studied to utilize the history information in different manners. In two benchmark datasets, i.e., PASCAL VOC2012 and MIRFlickr-25k, the average precision is improved by up to 10% in comparison with the state-of-the-art annotation methods.

关键词： computer vision image annotation DNN attention mechanism

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High-rate Modeling and Computing for Optical Systems: Gigapixel image Formation and X-ray Imaging Physics

High-rate Modeling and Computing for Optical Systems: Gigapi...

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作者： Gong, Qian Duke University

学位级别：Ph.D.

With the rapid development of computational sensing technologies, the volume of available sensing data has been increasing daily as sensor systems grow in scale. This is sometimes referred to as the "data deluge". Many physical computing applications have to spend great effort on meeting the challenges of this environment, which has prompted a need for rapid and efficient processing of massive datasets. Fortunately, many algorithms used in these applications can be decomposed and partially or fully cast into a parallel computing framework. This dissertation discusses three sensing models—gigapixel image formation, X-ray transmission and X-ray scattering—and proposes methods to formulate each task as a scalable and distributed problem which is adapted to the massively parallel architecture of Graphics processing Units (GPUs). For the gigapixel images, this dissertation presents a scalable and flexible image formation pipeline based on the MapReduce framework. The presented implementation was developed to operate on the AWARE multiscale cameras, which consist of microcamera arrays imaging through a shared hemispherical objective. The microcamera field-of-views slightly overlap and are capable of generating high-resolution and high dynamic range panoramic images and videos. The proposed GPU implementation takes advantage of the prior knowledge regarding the alignment between microcameras and exploits the multiscale nature of the AWARE image acquisition, enabling the rapid composition of panoramas ranging from display-scale views to gigapixel-scale full resolution images. On a desktop computer, a 1.6-gigapixel color panorama captured by the AWARE-10 can be delivered in less than a minute, while 720p and 1080p panoramas can be stitched at the video frame rate. We next present a pipeline that rapidly simulates X-ray transmission imaging via ray-tracing on GPU. This pipeline was initially designed for statistical analysis of X-ray threat detection in the context of aviatio

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parallel and Efficient Sensitivity Analysis of Microscopy image Segmentation Workflows in Hybrid Systems

Parallel and Efficient Sensitivity Analysis of Microscopy Im...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Barreiros, Willian, Jr. Teodoro, George Kurc, Tahsin Kong, Jun Melo, Alba C. M. A. Saltz, Joel Univ Brasilia Dept Comp Sci Brasilia DF Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA Emory Univ Biomed Informat Dept Atlanta GA 30322 USA

ISBN: (纸本)9781538623268

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2x. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46x on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

关键词： image segmentation Sensitivity analysis Optimization Algorithm design and analysis Runtime image analysis

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Analysis of Implementing Mobile Heterogeneous Computing for image Sequence processing

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2017年第10期11卷 4928-4947页

作者： Baek, Aram Lee, Kangwoon Kim, Jae-Gon Choi, Haechul Hanbat Natl Univ Dept Multimedia Engn Daejeon South Korea Korea Aerosp Univ Sch Elect Telecommun & Comp Engn Gyonggi Do South Korea

On mobile devices, image sequences are widely used for multimedia applications such as computer vision, video enhancement, and augmented reality. However, the real-time processing of mobile devices is still a challenge because of constraints and demands for higher resolution images. Recently, heterogeneous computing methods that utilize both a central processing unit (CPU) and a graphics processing unit (GPU) have been researched to accelerate the image sequence processing. This paper deals with various optimizing-techniques such as parallel processing by the CPU and GPU, distributed processing on the CPU, frame buffer object, and double buffering for parallel and/or distributed tasks. Using the optimizing techniques both individually and combined, several heterogeneous computing structures were implemented and their effectiveness were analyzed. The experimental results show that the heterogeneous computing facilitates executions up to 3.5 times faster than CPU-only processing.

关键词： image processing mobile platform heterogeneous computing embedded GPU GPGPU

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AdaLearner: An Adaptive distributed Mobile Learning System for Neural Networks 17

AdaLearner: An Adaptive Distributed Mobile Learning System f...

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IEEE/ACM 36th International Conference on Computer-Aided Design (ICCAD)

作者： Mao, Jiachen Qin, Zhuwei Xu, Zirui Nixon, Kent W. Chen, Xiang Li, Hai Chen, Yiran Duke Univ Durham NC 27706 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781538630938

Neural networks hold a critical domain in machine learning algorithms because of their self-adaptiveness and state-of-the-art performance. Before the testing (inference) phases in practical use, sophisticated training (learning) phases are required, calling for efficient training methods with higher accuracy and shorter converging time. Many existing studies focus on the training optimization on high-performance servers or computing clusters, e.g. GPU clusters. However, training neural networks on resource-constrained devices, e.g. mobile platforms, is an important research topic barely touched. In this paper, we implement AdaLearneran-adaptive distributed mobile learning system for neural networks that trains a single network with heterogenous mobile resources under the same local network in parallel. To exploit the potential of our system, we adapt neural networks training phase to mobile device-wise resources and fiercely decrease the transmission overhead for better system scalability. On three representative neural network structures trained from two image classification datasets, AdaLearner boosts the training phase significantly. For example, on LeNet, 1.75-3.37x speedup is achieved when increasing the worker nodes from 2 to 8, thanks to the achieved high execution parallelism and excellent scalability.

关键词： Training Computational modeling Data models Neural networks Load modeling parallel processing Mobile communication

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