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Pet image reconstruction using ANN-EM method 2

Pet image reconstruction using ANN-EM method

引用

2nd international conference on advanced Computing and Communication Systems, ICACCS 2015

作者： Iniyatharasi, P. Rajasekaran, M. Pallikonda Arun Prasath, T. Kannan, S. Department of ECE Kalasalingam University Virudhunagar Tamil Nadu India

ISBN: (纸本)9781479964383

Imaging is a broad field which covers all aspects of the analysis, modification, compression, visualization, and generation of images. There are at least two major areas in imaging science in which applied mathematics has a strong impact: image processing, and image reconstruction. In image processing the input is a (digital) image such as a photograph, while in image reconstruction the input is a set of data. image processing techniques treat an image and apply numerical algorithms to either improve the given image or to extract different features of it. image reconstruction refers to the technique used to create an image of the interior of a body (or region) non-invasively, from data collected on its boundary. Current imaging problems deal with the image quality and the computational tools used to create the image. The performance of ANN-EM algorithm is compared with the simultaneous version of co-ordinate descent algorithm (CD) and de-noising algorithm. algorithms are compared in terms of prediction and parameters like Peak signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Root Mean Square Error (RMSE) and Elapsed time for the algorithms. The results shows that ANN-EM based algorithm provides better reconstructed time compared to other two techniques. © 2015 IEEE.

关键词： Mean square error

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Removal of specular reflections in tooth color image by Perceptron neural nets

Removal of specular reflections in tooth color image by Perc...

引用

2010 2nd international conference on signal processing Systems, ICSPS 2010

作者： Lee, Seong-Taek Yoon, Tae-Ho Kim, Kyeong-Seop Kim, Kee-Deog Park, Wonse School of Biomedical Engineering Konkuk University Chungju Korea Republic of Department of Advanced General Dentistry Yonsei University Seoul Korea Republic of

ISBN: (纸本)9781424468911

This study presents the removal algorithm of specular reflections in a tooth color image to eliminate the specularities which degrades the performance of color image segmentation algorithms. Our proposed methodology includes two tasks: (i) automated detection of specular reflections by Perceptron neural nets and (ii) recursive corrections of the specularities by applying a smoothing spatial filter on the target pixels (specular regions) based on the decision of Perceptron. © 2010 IEEE.

关键词： Neural networks

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An image processing Application: Design of VLSI based Bilateral Filter using Distance Matrix Method 2

An Image Processing Application: Design of VLSI based Bilate...

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2nd IEEE international conference on advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Saranya, C. Vijayananth, M.V. Mouleeshwaran, S. Kaviyarasu, D. K.S.Rangasamy College of Technology Department of ECE Tiruchengode India

ISBN: (纸本)9781665493963

The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VLSI-based image filter architectures are essential. Numerous applications based on VLSI architecture experience problems with significant size components that result in a filter design error during the floating-point arithmetic stages. The increased complexity of the filter design's component list is the issue encountered in the VLSI architecture. Noise removal often involves using the image filtering technique. There is a greater need for performance-based hardware sorting algorithms for low power embedded real-Time image filtering applications. Noise often taints an image while being transmitted from one location to another. The purpose of the denoising technique is to reduce noise while keeping any potential signal features that may be significant. Compare to the prior method, the usage of multipliers can be reduced based on the techniques of distance matrix and hardware resource sharing. The speed of the proposed VLSI architecture for the bilateral filter can be faster than the existing approach. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Low-light image Enhancement Algorithm Based on Transformer 2

Low-light Image Enhancement Algorithm Based on Transformer

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2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Wang, Zeyu Qi, Yan Shenyang Ligong University Institute of Information Science and Technology Shenyang China

ISBN: (纸本)9798350368888

Low light image enhancement is an important task in computer vision. The Low-light images can adversely affect more advanced computer vision tasks such as target tracking, image segmentation, and target recognition due to the presence of large amounts of noise and low contrast. Therefore, research on low-light image enhancement is crucial. To address these issues, this paper delves into the comparison between traditional and non-traditional low-light image enhancement algorithms and has achieved the following results:We abandon the traditional approach based on a priori knowledge of images and instead redesign a new network structure for low-light image enhancement with the existing theoretical foundation for Transformer. The network performs information extraction in the channel dimension of the image through self-attention mechanism and then extracts the shallow feature information of the image through Shallow layer. After fusing these two types of information, more advanced information is extracted to guide the final image enhancement. Experimental results show that the redesigned network structure not only achieves better results but also has improved execution efficiency. © 2024 IEEE.

关键词： deep neural network low-light image enhancement Transformer theory

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Remote sensing image interpolation via the nonsubsampled contourlet transform

Remote sensing image interpolation via the nonsubsampled con...

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2nd international conference on image Analysis and signal processing, IASP'2010

作者： Xu, Tongyang Fang, Yong School of Comm. and Info. Eng. Key Lab of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Key Laboratory of Advanced Display and System Applications Shanghai China

ISBN: (纸本)9781424455553

A novel nonsubsampled contourlet Bi-linear interpolation algorithm for remote sensing image is proposed, which is based on the estimation of detail nonsubsampled contourlet coefficients at high resolution scales. The method employs Bi-linear interpolation algorithm to anticipate the high frequency component on high resolution from the high frequency component on low resolution. The experiment results demonstrate that the algorithm can get a higher PSNR and keep more image detail information than traditional interpolation algorithms. ©2010 IEEE.

关键词： Interpolation

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Proceedings of the 2nd international conference on Artificial Intelligence and Smart Energy, ICAIS 2022

Proceedings of the 2nd International Conference on Artificia...

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2nd international conference on Artificial Intelligence and Smart Energy, ICAIS 2022

ISBN: (纸本)9781665400527

The proceedings contain 318 papers. The topics discussed include: applications of artificial intelligence in biomedical image processing;change detection classification of buildings from satellite images of urban area using deep learning techniques;advanced driver assistant system using image processing;an effective high level capacity reversible data hiding in encrypted images;an experimental approach in color correction and contrast improvement for underwater images;virtual design method of indoor space based on 3d visual cycle modeling;dynamic simulation and fuzzy optimization of super high-rise building construction with intelligent video monitoring system;medical imaging using signal processing: a comprehensive review;face recognition using handcrafted features extraction techniques under occlusion condition;and application of virtual reality technology in the visual optimization of print advertisement based on image information mining system.

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image Defogging based on Combined Sparse Gradient Minimization and CNN Architecture 2

Image Defogging based on Combined Sparse Gradient Minimizati...

引用

2nd international conference on Microwave, Optical and Communication Engineering, ICMOCE 2023

作者： Jana, Anisha Sahoo, Upendra Kumar National Institute of Technology Department of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798350321609

Some environmental factors like haze or fog de-grades the quality of the image. These factors affect some real time processes such as object detection and recognition, automated vehicles and remote sensing which needs clear visible images for making critical decisions. Therefore, restoring the true image from the foggy image becomes significant. Now with the advancement in image processing, many image defogging and dehazing algorithms has been developed to improve the quality of the image. Many standard filtering techniques such as high boost filter, homomorphic filter can be used for image defogging but it fails to restore the foggy images completely so some advanced techniques like DCP, decomposition techniques, CNN based algorithms are used. The proposed work mainly focuses on CNN based algorithm for image defogging combined with SGM module for edge preservation. image quality assessment (IQA) is done to measure the quality of the defogged image. These performance metrics mainly includes mean squared error (MSE), structural similarity index metric (SSIM),peak signal to noise ratio (PSNR). © 2023 IEEE.

关键词： image enhancement

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An Improved Low-Light image Enhancement Algorithm Based on Deep Learning 2nd

An Improved Low-Light Image Enhancement Algorithm Based on D...

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2nd international conference on advanced Intelligent Technologies (ICAIT)

作者： Chen, Wen Hu, Chao Jiangxi Univ Sci & Technol Ganzhou 341099 Jiangxi Peoples R China NingboTech Univ Ningbo Zhejiang Peoples R China

ISBN: (纸本)9789811697357;9789811697340

At present, most low-light image enhancement algorithms rely on artificial design with a priori information and constraints, but it cannot accurately capture deep structural features of the images. In this paper, we propose an improved algorithm with EnlightenGAN as the base framework and consider the phenomenon that the distance reflected by Mean Square Error (MSE) is quite different from human intuition. By introducing Structural Similarity (SSIM) in the loss function, we try to improve the structural similarity of the enhanced images to make them more consistent with natural and human intuition. For the instability problem during Generative Adversarial Networks (GAN) training, the relatively loose and easy-to-compute Energy-Based GAN (EBGAN) is used instead of Wasserstein GAN (WGAN). The improved algorithm is finally tested by using Low-light image Enhancement (LIME) dataset as well as the test dataset of EnlightenGAN. The Peak signal to Noise Ratio (PSNR) and SSIM values of the images processed by using the modified algorithm are calculated and compared with other algorithms, and the results show the proposed method is effective.

关键词： Deep learning Generating adversarial networks image enhancement image processing

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On-line Hybrid Perceptual and Cryptographic image Hashing System 2

On-line Hybrid Perceptual and Cryptographic Image Hashing Sy...

引用

2nd international Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022

作者： Eldeeb, Tarek Gamal, Ahmed Kamel, Mahmoud Valeo Egypt

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Vision systems became safety critical in modern 'advanced Driver Assistance Systems', where software-controlled high-definition cameras and digital signal processors are the core components. Software verification using 'Hardware-In-Loop' is a growing need as a result. In this paper a new hybrid perceptual hashing system for on-line digital video images processing is presented. The system can detect frozen images, mono-color images and may be used in image comparison as well. The design uses minimal hardware and storage while maintaining a high operational frequency. The system performance is analyzed and functionally verified on an HDL implementation using an FPGA. © 2022 IEEE.

关键词： Knowledge engineering Machine vision System performance signal processing algorithms Software Hardware Safety

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Applications of Artificial Intelligence in Biomedical image processing 2

Applications of Artificial Intelligence in Biomedical Image ...

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2nd international conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Rajeyyagari, Sivaram Hung, Bui Thanh Chakrabarti, Prasun Shaqra University College of Computing and Information Technology Department of Computer Science Shaqra Saudi Arabia Thu Dau Mot University Data Analytics and Artificial Intelligence Laboratory Engineering Technology Faculty Thu Dau Mot Viet Nam Provost and Institute Endowed Distinguished Senior Chair Professor Techno India Njr Institute of Technology Rajasthan India

ISBN: (纸本)9781665400527

The biomedical applications of nanoparticles are growing in recent years. The uses range from diagnostic to therapeutic purposes, and researchers are constantly introducing new ways. Biosensors, miniaturization techniques, surface enhancement in implantable biomaterials, and better nanofibers in medical textiles are some of the contributions of biomedical nanotechnology. Therapeutic (medical) imaging successfully maps the anatomy of the human body. image enhancement, analysis, categorization, and image based representation are all possible areas of computer processing and analysis of medical images. Therapeutic image understanding uses computer vision, applied mathematics, signal analysis, and artificial intelligence to recognise objects in medical images. image processing is the use of computer algorithms to manipulate digital images. Hand-held transducers emit and receive pulsed acoustic waves for medical ultrasound imaging. In this study, a computer-aided diagnosis method is developed to help cytotechnicians locate and classify aberrant cells in cervical cytology images. With the outcomes of morphological and statistical edge based segmentation are compared to traditional segmentation techniques. The proposed segmentation methodology outperforms the other methods. This proposed technique's segmentation accuracy is 98%. © 2022 IEEE.

关键词： Artificial intelligence

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