image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretati...
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ISBN:
(纸本)9781665454025
image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretation by different automated imageprocessing system and human beings. Although, for low-contrast images (with lower dynamic range) traditional histogram equalization deteriorate quality of the output image by introducing washed-out form caused by too much brightness change. This paper presents histogram equalization based image enhancement method which is optimized using cuckoo search algorithm. Foremost purpose of the proposed method is to control over and under enhancement by calculating clip limits of the image histogram before performing histogram equalization. This method uses cuckoo search algorithm to automatically control the clip limit according to the image nature using improved fitness function. Effectiveness of the proposed method in comparison to other methods is analyzed using well known image quality metrics.
Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require performing a large number of conditional ...
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Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require performing a large number of conditional independence (CI) tests. The learning process is very time-consuming, especially for high-dimensional problems, which hinders the adoption of BNs to more applications. Existing works attempt to accelerate the learning process with parallelism, but face issues including load unbalancing, costly atomic operations and dominant parallel overhead. In this paper, we propose a fast solution named Fast-BNS on multi-core CPUs to enhance the efficiency of the BN structure learning. Fast-Bns is powered by a series of efficiency optimizations including (i) designing a dynamic work pool to monitor the processing of edges and to better schedule the workloads among threads, (ii) grouping the CI tests of the edges with the same endpoints to reduce the number of unnecessary CI tests, (iii) using a cache-friendly data storage to improve the memory efficiency, and (iv) generating the conditioning sets on-the-fly to avoid extra memory consumption. A comprehensive experimental study shows that the sequential version of Fast-BNS is up to 50 times faster than its counterpart, and the parallel version of Fast-Bns achieves 4.8 to 24.5 times speedup over the state-of-the-art multi-threaded solution. Moreover, Fast-BNS has a good scalability to the network size as well as sample size.
Optimization problems arising in signal and imageprocessing involve an increasingly large number of variables. In addition to the curse of dimensionality, another difficulty to overcome is that the cost function usua...
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Optimization problems arising in signal and imageprocessing involve an increasingly large number of variables. In addition to the curse of dimensionality, another difficulty to overcome is that the cost function usually reads as the sum of several loss or regularization terms, which are non-necessarily smooth and possibly composed with large-size linear operators. Proximal splitting approaches are fundamental tools to address such problems, with demonstrated efficiency in many applicative fields. In this paper, we present a new distributed algorithm for computing the proximity operator of a sum of non-necessarily smooth convex functions composed with arbitrary linear operators. Our algorithm relies on a primal-dual splitting strategy, and benefits from established convergence guaranties. Each involved function is associated with a node of a hypergraph, with the ability to communicate with neighboring nodes sharing the same hyperedge. Thanks to this structure, our method can be efficiently implemented on modern parallel computing architectures, distributing the computations on multiple nodes or machines, with controlled requirements for synchronization steps. Good numerical performance and scalability properties are demonstrated on a problem of video sequence denoising. Our code implemented in Julia is made available at https://github .com /MarinENSTA /distributed_julia_denoising. (C) 2022 Elsevier Inc. All rights reserved.
作者：
Wang, NingChen, FangYu, BoWang, LeiChinese Acad Sci
Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Chinese Acad Sci
Beijing 100049 Peoples R China Chinese Acad Sci
Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci
Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China
Superpixel segmentation algorithms are widely used in the imageprocessing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapid...
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Superpixel segmentation algorithms are widely used in the imageprocessing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories-even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implem
Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this pro...
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ISBN:
(纸本)9798350396386
Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades.
For traffic monitoring systems it is desirable to have a low-cost, low-maintenance, and reliable system to facilitate continuous and safe traffic flow. In line with this, we introduce distributed acoustic sensing (DAS...
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ISBN:
(纸本)9781728191423
For traffic monitoring systems it is desirable to have a low-cost, low-maintenance, and reliable system to facilitate continuous and safe traffic flow. In line with this, we introduce distributed acoustic sensing (DAS) for traffic monitoring applications. In DAS systems, series of light pulses are transmitted along a fiber-optic cable and the back-scattered light, which is affected by the mechanical strain of the fiber-optic cables due to ground vibrations, is measured and analyzed. With fiber-optic cables installed parallel to a highway where ground vibrations are induced by passing vehicles, traffic information such as vehicle flow and average speeds can be estimated from the DAS signals. In this paper, we present an algorithm based on imageprocessingmethods that estimates traffic information from image representations generated from 1-minute segments of DAS signals. We tested the results of the algorithm on DAS data recording on a real highway against reference traffic data measured by road-side sensors.
The proceedings contain 16 papers. The special focus in this conference is on Signal and imageprocessing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Ide...
ISBN:
(纸本)9789813369658
The proceedings contain 16 papers. The special focus in this conference is on Signal and imageprocessing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction Techniques;the Effect of Different Feature Selection methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health Applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral image Thresholding.
This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastruct...
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This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastructure-agnostic distributed and parallel applications. In Kulla, applications, dependencies and environment settings, are mapped with construction units called Kulla-Blocks. A parallel programming model enables developers to couple those interoperable structures for creating constructive structures named Kulla-Bricks. In these structures, continuous dataflow and parallel patterns can be created without modifying the code of applications. methods such as Divide&Containerize (data parallelism), Pipe&Blocks (streaming), and Manager/Block (task parallelism) were developed to create Kulla-Bricks. Recursive combinations of Kulla instances can be grouped in deployment structures called Kulla-Boxes, which are encapsulated into VCs to create infrastructure-agnostic parallel and/or distributed applications. Deployment strategies were created for Kulla-Boxes to improve the IT resource profitability. To show the feasibility and flexibility of this model, solutions combining real-world applications were implemented by using Kulla instances to compose parallel and/or distributed system deployed on different IT infrastructures. An experimental evaluation based on use cases solving satellite and medical imageprocessing problems revealed the efficiency of Kulla model in comparison with some traditional state-of-the-art solutions. (C) 2020 Published by Elsevier Inc.
The proceedings contain 59 papers. The topics discussed include: generative adversarial networks to generalize urban areas in topographic maps;3d city model as a first step towards digital twin of Sofia city;from arch...
The proceedings contain 59 papers. The topics discussed include: generative adversarial networks to generalize urban areas in topographic maps;3d city model as a first step towards digital twin of Sofia city;from architectural survey to continuous monitoring: graph-based data management for cultural heritage conservation with digital twins;ontology-based data mapping to support planning in historical urban centers;determining the suitable location for the metallurgical and steel processing industries in Mongolia using GIS-based multi-criteria analysis methods;spatio temporal data cube applied to ais containerships trend analysis in the early years of the belt and road initiative – from global to local scale;impact analysis of accidents on the traffic flow based on massive floating car data;and using systems of parallel and distributed data processing to build hydrological models based on remote sensing data.
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This ch...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual imageprocessing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.
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