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A No-reference image Quality Assessment Algorithm Based on Human Visual Perception Process Reconstruction 4

A No-reference Image Quality Assessment Algorithm Based on H...

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4th international conference on Video, signal and image processing, VSIP 2022

作者： Zhang, Shuo Su, Ping Ma, Jianshe Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (纸本)9781450397810

Traditional no-reference image quality assessment algorithms mostly focus on the objective indicators of the image, such as the IE (information entropy) and clarity of the image. These indicators can reflect the quality of the image at the objective level to a certain extent, but it cannot well reflect the human visual evaluation of the image. In fact, it's still a relatively difficult problem to obtain the human visual evaluation of the image through a no-reference image quality assessment algorithm, considering the complexity of the imaging mechanism of the HVS (Human Visual System), for example, the connection mode of photoreceptor cells and ganglion cells which still cannot be well expressed by any specific model, along with the individual differences of human visual perception. This paper starts with the perception process reconstruction of the HVS, uses the similarity between the reconstructed image and the original image to reflect whether the image conforms to the request of human visual perception, carries out parameter optimization and correlation verification. Finally, it is found that the proposed algorithm has better relevance, lightness and universality than the existing algorithms, and can more effectively restore the scoring result of human visual perception of the test image. © 2022 ACM.

关键词： image reconstruction

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image denoising algorithms using norm minimization techniques 2

Image denoising algorithms using norm minimization technique...

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2nd international conference on Computer Science's Complex Systems and their Applications, ICCSA 2021

作者： Diffellah, Nacira Bekkouche, Tewfik Hamdini, Rabah Dept. of Electronics Faculty of Technology University of Bordj Bou Arreridj Bordj Bou Arreridj34000 Algeria Dept. of Electromechanics Faculty of Technology University of Bordj Bou Arreridj Bordj Bou Arreridj34000 Algeria Dept. of Electronics Faculty of Technology University of Saad Dahlab Blida 01 Blida09000 Algeria

image denoising is one of the fundamental image processing problems. Noise removal is an important step in the image restoration process. In this paper, firstly we develop and implement two different image denoising algorithms based on norm minimization, namely 1 and 2-regularization applied to images contaminated by Gaussian noise. Then, after their discretization and implementation, we perform a comparison between the two methods using several test images. Through this study, the algorithm which minimizes 2-norm of gradient of image has a unique solution and it's easy to implement, but it doesn't accept contour discontinuities, causing the obtained solution to be smooth. The 2-norm will blur the edges of the image. In order to preserve sharp edges, 1-norm is introduced. There are different methods to solve the problem of energy minimization. In this work, we have chosen the discretization finite difference method before applying the gradient descent algorithm to optimize the signal (2D grayscale images) denoising functionality. Experiments results, show that 1 regularization encourages image smoothness while allowing for presence of jumps and discontinuities, a key feature for image processing because of the importance of edges in human vision. © 2021.

关键词： image denoising

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Single Color image Dehazing Based on Variational Model 6

Single Color Image Dehazing Based on Variational Model

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Zhao, Shengnan Yang, Hao Zhao, Yonghao Yancheng Teachers University School of Information Engineering Yancheng China

ISBN: (纸本)9780738143705

Combining variational approach with the atmospheric attenuation model, two single color image dehazing algorithms on the basis of dark channel prior and MTV model are proposed which are called H-MTV model and NL-H-MTV model. Then, we use Dual Bregman algorithm to calculate the H-MTV model and Split Bregman algorithm to calculate the NL-H-MTV model. We conducted three experiments to adjust the parameters. Finally, comparing the proposed models with He algorithm and Kimmel Retinex algorithm. Experimental results show that the proposed methods can remove fog while keeping texture features effectively. © 2021 IEEE.

关键词： Color

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Wirelessly Controlled Plant Health Monitoring and Medicate System Based on IoT Technology 1st

Wirelessly Controlled Plant Health Monitoring and Medicate S...

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1st international conference on advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Ayalew, Lijaddis Getnet Mattihalli, Channamallikarjuna Asmare, Fanuel Melak Bahir Dar Univ Fac Elect & Comp Engn Bahir Dar Inst Technol Bahir Dar Ethiopia

ISBN: (纸本)9783030960407;9783030960391

The availability of good quality fruits and vegetables is paramount in preventing starvation and minimizing outbreaks of diseases which leads to improving quality of life. One of the major obstacles of the mentioned availability is plant leaf disease. Although manpower plays a vital role in detecting such problems it is time-intensive, expensive, and very inefficient. Thus, developing a mechanism to vigorously monitor leaf's health and detect diseases of plant leaves at early stages is mandatory so that one can produce plenty. In this contribution, a system that detects leaf disease is developed using image processing algorithms, the k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perception (MLP) machine learning algorithms are compared based on plant disease detection and classification systems performances. We also developed a prototype of simple-to-install technology that can recognize leaf diseases and allow medicine flow based on the results. This paper presents a smart plant health monitoring system that takes into account humidity, temperature, and soil contents.

关键词： image processing IoT KNN MLP SVM

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image processing and Control of Tracking Intelligent Vehicle Based on Grayscale Camera 5

Image Processing and Control of Tracking Intelligent Vehicle...

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5th IEEE international conference on image processing Applications and Systems (IPAS)

作者： Zhang, Jian Liu, Yufan Li, Ao Zeng, Jinshan Xie, Hongtu Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665462198

In order to realize the rapid and stable recognition and automatic tracking of various complex roads by the intelligent vehicles, this paper proposes image processing and cascade Proportion Integration Differentiation (PID) steering and speed control algorithms based on CMOS grayscale cameras in the context of the national college student intelligent vehicle competition. First, the grayscale image of the track is acquired by the grayscale camera. Then, the Otsu method is used to binarize the image, and the information of black boundary guide line is extracted. In order to improve the speed of the race, various track elements in the image are identified and classified, and the deviation between the actual centerline position and the ideal centerline position of the intelligent vehicle is calculated. Third, the discrete incremental cascade PID control algorithm is used to calculate the pulse width modulation (PWM) signal corresponding to the deviation. And the PWM signal is acted on the steering motor through the driving circuit, driving the intelligent vehicle to always drive along the middle road, so as to achieve the purpose of automatic tracking guidance. Experiments prove that the intelligent vehicle of this design can identify complex roads quickly and in a stable way, accurately complete automatic tracking, and obtain higher speed performance.

关键词： tracking intelligent vehicle proportional integral derivative control CMOS grayscale camera binary image image processing

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Research of intertidal remote sensing image fusion method based on logical block 7th

Research of intertidal remote sensing image fusion method ba...

引用

7th international conference on signal and Information processing, Networking and Computers, ICSINC 2020

作者： Xing, Jian Wu, Lina Dong, Shilin Institute of Spacecraft System Engineering CAST Beijing100094 China Institute of Spacecraft Application System Engineering CAST Beijing100094 China

ISBN: (纸本)9789813341012

The intertidal zone refers to the coast between the average highest and lowest tidal levels, which is an important area for researching the dynamics of modern sea-land environments and utilizing the marine resources. The intertidal zone is affected by the dual forces of sea and land, and also human activities. The existing land area remote sensing image processing methods are not completely suitable for intertidal zones, and the accuracy and efficiency of image fusion processing methods need to be improved. A method of the fusion of large scale data remote sensing images for the land and sea intertidal zone is proposed. The logical block is used to divide the image and an image pyramid is established. Through the improvement of different fusion algorithms, the fusion of large scale data remote sensing images is achieved with a high accuracy and efficiency. The experiment was developed to prove that the IHS-NMF algorithm based on the logical block is effective in optimizing large scale remote sensing images. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

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Providing a desired quality of BPG compressed images for FSiM metric 3

Providing a desired quality of BPG compressed images for FSi...

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3rd IEEE international conference on advanced Trends in Information Theory, ATIT 2021

作者： Li, Fangfang Lukin, Vladimir Okarma, Krzysztof Fu, Yanjun Dept. of information-Communication Technologies National Aerospace University – KhAi Kharkiv Ukraine Nanchang Hangkong University Nanchang China Dept. of Signal Processing and Multimedia Engineering West Pomeranian University of Technology in Szczecion Szcaecin Poland

ISBN: (纸本)9781665438452

it is a common task in image lossy compression to provide a reasonable trade-off between a desired compression ratio (as large as possible) and an appropriate (preset according to a chosen metric) quality. Recently, a Better Portable Graphics (BPG) coder has been proposed that might become a new standard. A quality parameter Q is used in it to vary image quality and compression ratio. However, a given Q for particular images to be compressed produces quite different compression ratios and quality measured by a given quality metric. To provide a given quality of compressed images characterized by a visual quality metric FSiMc, we propose and analyze a two-step algorithm based on preliminary obtained average rate/distortion curve and its linear approximation. For this algorithm, the second step that calculates the improved estimate of Q and employs it for final compression allows decreasing variance of the produced FSiMc by about one order of magnitude compared to the first compression step where Q is set according to the average rate-distortion curve. The algorithm is applied for a wide set of desired values of FSiMc. it is also tested for a set of images not used in obtaining the average rate/distortion curve. © 2021 iEEE.

关键词： image compression

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Unsupervised image Segmentation model based on W Net architecture and Conditional Random Field for Underwater images 7

Unsupervised Image Segmentation model based on W Net archite...

引用

7th international conference on advanced Computing and Communication Systems, ICACCS 2021

作者： Saleh, Esra Sorkty Mithun Haridas, T.P. Supriya, M.H. Cochin University of Science and Technology Department of Electronics Kochi India

ISBN: (纸本)9781665405201

Under water imaging has been extensively used in monitoring the marine ecosystems for quite some time. The automated learning systems that are much explored in marine ecosystem are mainly supervised methods. Supervised methods even though require less human intervention, still demands considerable manual effort for the careful design of the dataset. The large amount of labelled and annotated data needed for proper learning, as well as the attention required for designing the ground truth dataset for efficient training, makes the supervised learning approach less attractive, especially in the underwater scenarios. This work explores the possibility of unsupervised learning methods for the analysis of the underwater images using segmentation algorithms, which does not need ground truth annotations and is much more scalable. images and videos from Fish4Knowledge database is utilized for this work to train the unsupervised architecture based on W-Net architecture. The autoencoder architecture is analyzed considering the segmented output from the first half and reconstructed image from the second half using soft N-cut loss and reconstruction loss respectively. Post processing method of Conditional Random Field is also applied for smoothing the initial segmentation. The performance is analyzed using the learning curves and the metrics such as VI, PRI and SC. © 2021 IEEE.

关键词： Random processes

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Multi-threshold laser stripe image segmentation based on improved black widow algorithm

Multi-threshold laser stripe image segmentation based on imp...

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2023 international conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Xu, Jinli Liu, Tianyu Lu, Jie School of Electrical and Mechanical Engineering Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510666351

To solve the problems of low segmentation accuracy, mis-segmentation, and exponential increase of computation time with the rise of the number of thresholds when multi-threshold image segmentation is performed on images of multiple laser stripes in the structured lights windshield assembly detection environment, an improved black widow optimization algorithm (IBWOA) for multi-threshold laser stripe image segmentation is proposed. In the population initialization stage, Logistic chaotic mapping is used to enhance the initial population randomness. In the position updating stage, an adaptive step size strategy is introduced to improve the search speed and accuracy in the later stage, and individual variation based on the reverse learning strategy is introduced to avoid falling into local optimal solutions. Compared with existing swarm intelligence optimization algorithms, the results show that IBWOA has better search speed, accuracy, and escape ability to jump out of locally optimal solutions in multi-threshold laser stripe image segmentation. © 2023 SPIE.

关键词： image segmentation

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An Optimizing Multilevel Thresholding for image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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16th international conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2020 in conjunction with the 13th international conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： image segmentation

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