Image And Video Processing
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Author(s):  
А.И. Максимов

В работе предложен метод повышения пространственного разрешения по серии кадров низкого разрешения, использующий для формирования результирующего изображения значения погрешностей восстановления в точке каждого кадра. Метод объединяет в себе результаты многолетних исследований автора в области повышения качества изображений и видеозаписей. Предложенный метод разрабатывался для решения прикладных задач криминалистической экспертизы видеозаписей и предназначен для повышения визуального качества плоского локального объекта, находящегося близко к центру кадра. Метод состоит из трех этапов. Первый этап - процедура сверхразрешающего восстановления в каждом кадре с учетом непрерывно-дискретной модели наблюдения сигнала с сохранением сведений об ошибке такого восстановления в дополнительный канал обработки изображения. Второй – геометрическое согласование восстановленных кадров с применением геометрического преобразования к дополнительному каналу обработки. Третий – взвешенное оптимальное по критерию минимизации среднеквадратической ошибки комплексирование кадров. Преимуществами предлагаемого метода являются оценка погрешности восстанавливаемого изображения в каждой точке, а также учет искажений изображений в непрерывной области. В работе проведено экспериментальное исследование ошибки восстановления предлагаемого метода, полученные результаты сравнивались со случаем, не использующим авторские находки предлагаемого метода, - усредняющим комплексированием линейно интерполированных кадров. Линейная интерполяция была взята, поскольку она также вписывается в фильтровую модель восстановления изображения на первом этапе работы метода. Полученные результаты демонстрируют превосходство предлагаемого метода. In this paper, a method for multi-frame superresolution is proposed. It exploits the values ​​of the recovery errors at the point of each frame to form the resulting high-resolution image. The method combines the results of many years of author's research in the field of image and video processing. The proposed method aims to apply to forensic tasks of video analysis. The method improves the visual quality of a flat local object located close to the center of the frame. The method consists of three stages. The first stage is the procedure of optimal super-resolution recovery of each frame with the use of the continuous-discrete observation model. During this stage, the recovery errors are stored in an additional image channel. The second stage is the frames registration. A geometric transformation is also applied to the additional channel during this stage. The final stage is the weighted optimal fusing. The advantages of the proposed method are the estimation of the error of the restored image at each point and taking into account the image degradations in the continuous domain. Experimental research of the reconstruction error of the method was carried out. The results were compared with the case that does not use the novel features of the proposed method - averaging fusing of linear interpolated frames. Linear interpolation was chosen as it also fits into the filtering model of image recovery of the method's first stage. The obtained results show that the proposed method outperforms the other one.


Author(s):  
Chamin Morikawa ◽  
Michihiro Kobayashi ◽  
Masaki Satoh ◽  
Yasuhiro Kuroda ◽  
Teppei Inomata ◽  
...  

2021 ◽  
Author(s):  
Fatema Rashid

With the tremendous growth of available digital data, the use of Cloud Service Providers (CSPs) are gaining more popularity, since these types of services promise to provide convenient and efficient storage services to end-users by taking advantage of a new set of benefits and savings offered by cloud technologies in terms of computational, storage, bandwidth, and transmission costs. In order to achieve savings in storage, CSPs often employ data dedplication techniques to eliminate duplicated data. However, benefits gained through these techniques have to balanced against users' privacy concerns, as these techniques typically require full access to data. In this thesis, we propose solutions for different data types (text, image and video) for secure data deduplication in cloud environments. Our schemes allow users to upload their data in a secure and efficient manner such that neither a semi-honest CSP nor a malicious user can access or compromise the security of the data. We use different image and video processing techniques, such as data compression, in order to further improve the efficiency of our proposed schemes. The security of the deduplication schemes is provided by applying suitable encryption schemes and error correcting codes. Moreover, we propose proof of storage protocols including Proof of Retrievability (POR) and Proof of Ownership (POW) so that users of cloud storage services are able to ensure that their data has been saved in the cloud without tampering or manipulation. Experimental results are provided to validate the effectiveness of the proposed schemes.


2021 ◽  
Author(s):  
Fatema Rashid

With the tremendous growth of available digital data, the use of Cloud Service Providers (CSPs) are gaining more popularity, since these types of services promise to provide convenient and efficient storage services to end-users by taking advantage of a new set of benefits and savings offered by cloud technologies in terms of computational, storage, bandwidth, and transmission costs. In order to achieve savings in storage, CSPs often employ data dedplication techniques to eliminate duplicated data. However, benefits gained through these techniques have to balanced against users' privacy concerns, as these techniques typically require full access to data. In this thesis, we propose solutions for different data types (text, image and video) for secure data deduplication in cloud environments. Our schemes allow users to upload their data in a secure and efficient manner such that neither a semi-honest CSP nor a malicious user can access or compromise the security of the data. We use different image and video processing techniques, such as data compression, in order to further improve the efficiency of our proposed schemes. The security of the deduplication schemes is provided by applying suitable encryption schemes and error correcting codes. Moreover, we propose proof of storage protocols including Proof of Retrievability (POR) and Proof of Ownership (POW) so that users of cloud storage services are able to ensure that their data has been saved in the cloud without tampering or manipulation. Experimental results are provided to validate the effectiveness of the proposed schemes.


2021 ◽  
Vol 11 (10) ◽  
pp. 4632
Author(s):  
Vladimir Kulyukin

In 2014, we designed and implemented BeePi, a multi-sensor electronic beehive monitoring system. Since then we have been using BeePi monitors deployed at different apiaries in northern Utah to design audio, image, and video processing algorithms to analyze forager traffic in the vicinity of Langstroth beehives. Since our first publication on BeePi in 2016, we have received multiple requests from researchers and practitioners for the datasets we have used in our research. The main objective of this article is to provide a comprehensive point of reference to the datasets that we have so far curated for our research. We hope that our datasets will provide stable performance benchmarks for continuous electronic beehive monitoring, help interested parties verify our findings and correct errors, and advance the state of the art in continuous electronic beehive monitoring and related areas of AI, machine learning, and data science.


2021 ◽  
Author(s):  
David Moss

<p>Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.</p>


2021 ◽  
Author(s):  
David Moss

<p>Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.</p>


2021 ◽  
Author(s):  
Mengxi Tan ◽  
Xingyuan Xu ◽  
Andreas Boes ◽  
Bill Corcoran ◽  
Jiayang Wu ◽  
...  

Abstract Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.


Author(s):  
mengxi tan ◽  
xingyuan xu ◽  
David Moss

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10&rsquo;s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with &gt;90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.


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