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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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images super-resolution using improved generative adversarial networks 3

Images super-resolution using improved generative adversaria...

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3rd International conference on Intelligent Control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2021

作者： Hu, Yuliang Jing, Mingli Jiao, Yao Sun, Kun Xi'An Shiyou University School of Electrical Engineering Xi'an China Shaanxi Provincial Key Lab of Oil and Gas Well Measurement and Control Technology Xi’an China

ISBN: (纸本)9781665437158

image super-resolution (ISR) is an important image processing technology to improve image resolution in computer vision tasks. The purpose of this paper is to study the super-resolution reconstruction of single image based on the depth learning method. Aiming at the problem that the existing pixel loss-based super-resolution image reconstruction algorithms have poor reconstruction effect on high-frequency details, such as textures, a lighter algorithm is proposed on the basis of the existing deep learning method (SRGAN). Firstly, the feedback structure is applied in the generator to process the feedback information and enhance the high frequency information of the image. Secondly, a general residual feature aggregation framework (RFA), is applied to make full use of the residual information of each layer to improve the quality of the SR image. Finally, the solution space of the function is further reduced and the image quality is improved by using a new loss function. The algorithms are implemented on pytorch framework. The experimental results on VOC2012 data sets show that, compared with the original SRGAN algorithm, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed algorithm on the benchmark data set Set5 are improved by 0.83dB and 0.028, respectively, on Set14, the PSNR and SSIM of the proposed algorithm are improved by 0.56dB and 0.009, on Urban100, the PSNR and SSIM of the proposed algorithm are improved by 0.51dB and 0.031, on BSD100, the PSNR and SSIM of the proposed algorithm are improved by 0.33dB and 0.014, and compared with other improved algorithms, the effect of this algorithm is also better than other algorithms. © 2021 IEEE.

关键词： Generative adversarial networks

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Performance Evaluation of Multiwavelet Transform for Single image Dehazing 3rd

Performance Evaluation of Multiwavelet Transform for Single ...

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3rd EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Sankar, M. Ravi Srinivas, P. Praveena, V. Bhavani, D. Suseela, M. Sri Uma Srinivas, Y. Rao, Ch. Venkateswara Department of ECE Sasi Institute of Technology and Engineering Tadepalligudem India Department of ECE SRKR Engineering College Bhimavaram534202 India Department of ECE Vishnu Institute of Technology Bhimavaram534202 India

ISBN: (纸本)9783031289743

images captured in poor lighting conditions (such haze, fog, mist, or smog) have a lower level of visibility because air particles deflect light. Single picture dehazing techniques can restore clarity to a single hazy image. Even though Dark channel prior (DCP) has been the most used method for image dehazing algorithms, it has poor picture quality and requires a lot of adjustments for real time applications such as;computer vision, object detection. However, previous studies such as;Dark Channel Prior (DCP) on image dehazing agonises from a gigantic processing time and sleaze of the original image. In addition, the dimensions of the images have a significant effect on the performance of the dehazing algorithms. Hence, the main motive of this work is to minimize the computational overload either by reducing the size of the image or by accurate transmission map estimation without compromising the image quality. Hence, this work is focused on reducing the computational time for image dehazing through multilevel discrete (Haar) wavelet transform, in which the image dimensions has been reduced without degrading quality. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 90% when compared to DCP without degrading image quality. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Wavelet transforms

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An Intelligent image processing System for Enhancing Blood Vessel Segmentation on Low-Power SoC 23rd

An Intelligent Image Processing System for Enhancing Blood V...

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23rd International conference on Embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2023

作者： Alsharari, Majed Mai, Son T. Garnier, Romain Reaño, Carlos Woods, Roger Queen’s University Belfast Belfast Northern IrelandBT9 5AF United Kingdom Universitat de València Valencia46100 Spain Jouf University Sakaka72341 Saudi Arabia

ISBN: (纸本)9783031460760

Machine learning offers the potential to enhance real-time image analysis in surgical operations. This paper presents results from the implementation of machine learning algorithms targeted for an intelligent image processing system comprising a custom CMOS image sensor and field programmable gate array. A novel method is presented for efficient image segmentation and minimises energy usage and requires low memory resources, which makes it suitable for implementation. Using two eigenvalues of the enhanced Hessian image, simplified traditional machine learning (ML) and deep learning (DL) methods are employed to learn the prediction of blood vessels. Quantitative comparisons are provided between different ML models based on accuracy, resource utilisation, throughput, and power usage. It is shown how a gradient boosting decision tree (GBDT) with 1000 times fewer parameters can achieve comparable state-of-the-art performance whilst only using a much smaller proportion of the resources and producing a 200 MHz design that operates at 1,779 frames per second at 3.62 W, making it highly suitable for the proposed system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Blood vessels

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image-Based Physics Rendering for 3D Surface Reconstruction: A Survey 1

引用

3rd International conference on 3D Imaging Technologies - Multidimensional Signal processing and Deep Learning (3DIT-MSP and DL)

作者： Fang, Da Qin, Zhibao Liang, Shaojun Luo, Renbo Yunnan Normal Univ Yunnan Key Lab Opt Elect Informat Technol Kunming Yunnan Peoples R China

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

Obtaining 3D surface information and physical material information of an object from images is an essential research prospect in computer vision and computer graphics. image-based 3D reconstruction is to extract the 3D depth information of the scene and objects from single or multiple images through specific algorithms to reconstruct the 3D model of objects or locations with robust realism, which has fast reconstruction speed, simple equipment, realistic effect, and minor technical data, which can better realize the virtualization of natural objects.

关键词： image 3D reconstruction Physics rendering

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Multispectral image Compression Algorithm Based on Silced Convolutional LSTM 10th

Multispectral Image Compression Algorithm Based on Silced Co...

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10th International conference on Communications, Signal processing, and systems

作者： Wang, Kang Zhang, Ning Hu, Kedi Cao, Tongbo Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China AVIC Xian Flight Automat Control Res Inst Facri Natl Key Lab Sci & Technol Aircraft Control Xian 710065 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learningmethod which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates.

关键词： Sliced convolutional LSTM Multispectral image Spectral features

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Comparative Analysis of Clustering algorithms Based on Pixel image processing Using the Ssim Metric and Visual Analysis

Comparative Analysis of Clustering Algorithms Based on Pixel...

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International conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Kseniia Alexandrovna Potapova The Institute of Information Technologies MIREA – Russian Technological University Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper considers the problem of clustering pixels of a color raster image. The task is to compare the effectiveness of three different clustering methods: k-means, DBSCAN, agglomerative clustering. The k-means and agglomerative clustering algorithms consider different numbers of clusters: 2, 5, 10, 15, 20. The effectiveness is assessed using the SSIM metric and visual analysis of the resulting images.

关键词： Measurement Visualization Automation image color analysis Clustering algorithms Random access memory Process control Control systems Mathematical models Energy efficiency

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Automatic Architectural Drawing Labelling Using Deep Convolutional Neural Network 13th

Automatic Architectural Drawing Labelling Using Deep Convolu...

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13th International conference on Sustainability and Energy in Buildings (SEB)

作者： Sajjadian, Seyed Masoud Jafari, Mina Chen, xin Edinburgh Napier Univ Sch Engn & Built Environm Edinburgh Midlothian Scotland Univ Nottingham Sch Comp Sci Intelligent Modelling & Anal Grp Nottingham England

ISBN: (纸本)9789811662690;9789811662683

Architectural designers and technologists are able to make an assessment on buildability, thermal and hygrothermal performance of design details. To process drawings, human vision segments, classifies and distinguishes the drawing objects on the basis of their knowledge. With the rapid advancement of Artificial Intelligence methods, vast opportunities become available for performing tasks that used to require human intelligence or assistance by humans. image processing and analysis is one of these tasks that consists of the manipulation of images using algorithms. There are various applications in different fields, and the use of it is increasing exponentially. This paper explores the use of image processing in identifying building materials in order to check compliance with building regulations and identify anomalies. In this paper, an encoder-decoder based deep convolutional neural network (DRU-net) for image segmentation is applied on architectural images to segment various materials including insulations, bricks and concrete in the conceptual development phase. An experimental analysis is performed on numerous detail drawings and an evaluation is made by mathematical models.

关键词： Architectural detail drawings Deep learning image processing

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Deep Fourier Up-Sampling 36

Deep Fourier Up-Sampling

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36th conference on Neural Information processing systems (NeurIPS)

作者： Zhou, Man Yu, Hu Huang, Jie Zhao, Feng Gu, Jinwei Loy, Chen Change Meng, Deyu Li, Chongyi Chinese Acad Sci Hefei Inst Phys Sci Hefei Peoples R China Univ Sci & Technol China Hefei Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Xi An Jiao Tong Univ Xian Peoples R China Pazhou Lab Huangpu Huangpu Peoples R China SenseBrain Technol SenseTime Res USA Princeton NJ USA

ISBN: (纸本)9781713871088

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (e.g., interpolation, transposed convolution, and un-pooling) heavily depend on local pixel attention, in-capably exploring the global dependency. In contrast, the Fourier domain obeys the nature of global modeling according to the spectral convolution theorem. Unlike the spatial domain that performs up-sampling with the property of local similarity, up-sampling in the Fourier domain is more challenging as it does not follow such a local property. In this study, we propose a theoretically sound Deep Fourier Up-Sampling (FourierUp) to solve these issues. We revisit the relationships between spatial and Fourier domains and reveal the transform rules on the features of different resolutions in the Fourier domain, which provide key insights for FourierUp's designs. FourierUp as a generic operator consists of three key components: 2D discrete Fourier transform, Fourier dimension increase rules, and 2D inverse Fourier transform, which can be directly integrated with existing networks. Extensive experiments across multiple computer vision tasks, including object detection, image segmentation, image de-raining, image dehazing, and guided image super-resolution, demonstrate the consistent performance gains obtained by introducing our FourierUp. Code is available at https://***/.

关键词： image segmentation

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Real-time transmission audio and video data stream gateway platform for monitoring and management 2

Real-time transmission audio and video data stream gateway p...

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2nd International conference on Electronic Information Engineering and Computer Communication, EIECC 2022

作者： Zhao, Hongbo Li, Tong Beijing institute of Graphic Communication Beijing Key Laboratory of Signal and Information Processing China

ISBN: (纸本)9781510663121

Nowadays, with the rapid development of Internet technology and the emergence of converged media, the rapid transmission of news and information and the ways of transmission are increasingly diversified, and the transmission of video information is gradually developing forward and entering the mainstream of society. Video communication has become an integral part of the new era. This paper proposes a conference mode, which is convenient for the video conference terminal to connect to the video conference software, and also convenient for later operation and maintenance. Our video conference terminal uses Polycom's hardware equipment, our software client uses Huawei cloud conference, and our management platform is managed by the intelligent edge platform of the Internet of Things. The conference system, which supports HD real-time video transmission, has lower delay when resharing the screen than the conference system developed by Tencent conference. This system maintains stable transmission even in the case of serious renetwork packet loss. © 2023 SPIE.

关键词： image communication systems

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Coalitions of UAVs for Victims Localization in Post-Avalanche Events using Advanced image processing Techniques 22

Coalitions of UAVs for Victims Localization in Post-Avalanch...

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12th Hellenic conference on Artificial Intelligence (SETN)

作者： Trigkas, Nikolaos Chalkiadakis, Georgios Tech Univ Crete Khania Greece

ISBN: (纸本)9781450395977

This paper puts forward a novel coalition formation framework to enable teams of Unmanned Aerial Vehicles (UAVs) to assist in victims localization and rescue in the case of post-avalanche events. Our framework consists of multiple components. First, a novel initial UAVs placement algorithm (adapting a known Brain-Storming Optimization algorithm to our setting);second, a novel coalition structure generation protocol that allows for the "online" calculation of coalition values that will eventually guide the rescue effort;and third, a simple but effective opinion aggregation protocol, that can be used to prioritize certain rescue operations in the event of "ambiguous" findings. Moreover, for the recognition task itself we employ two image recognition algorithms, used for the first time for the analysis of post-avalanche Search & Rescue Operations images. Our experimental evaluation verifies the applicability and effectiveness of our framework and its individual components.

关键词： Search & Rescue image processing Multi-Agent systems

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