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Quality evaluation of speckle-noise-contaminated computer-generated holograms by using deep neural network 38

Quality evaluation of speckle-noise-contaminated computer-ge...

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conference on Practical Holography xxxVIII - Displays, Materials, and applications

作者： Min, Kyosik Park, Jae-Hyeung Inha Univ Dept Informat & Commun Engn Inha Ro 100 Incheon South Korea Inha Univ Dept Elect & Comp Engn Inha Ro 100 Incheon South Korea

ISBN: (纸本)9781510670815;9781510670808

Computer-generated hologram (CGH) techniques have advanced in the field of display technology, capable of reproducing three-dimensional images. The reconstructed three-dimensional images of the CGHs, however, usually contain speckle noises due to random phase distribution applied in the CGH synthesis. The random distribution of the speckle noise makes the traditional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM), which are generally used in image quality evaluation, become less reliable. In this paper, we propose a novel method to evaluate the speckled CGHs using a deep neural network.

关键词： Computer-generated hologram Holography Speckle noise image processing

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On evaluating quality of experience in mixed immersive communication scenarios 47

On evaluating quality of experience in mixed immersive commu...

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conference on applications of Digital image processing xLVII

作者： Upenik, Evgeniy Lazzarotto, Davi Mange, Robin Bello, Javier Iturrioz, Kepa Martinez, Anjo Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland Imverse SA Route Longeraie 7 CH-1110 Morges Switzerland

ISBN: (纸本)9781510679344;9781510679351

This paper introduces a methodology for evaluating quality of experience in mixed immersive communication systems. It focuses on assessing the impact of advanced 3D capture techniques, immersive eye-sensing light field displays, and efficient compression mechanisms in mixed setups where terminals offer different imaging modalities. The paper methodically investigates the influence of the above technologies on user experience in various communication scenarios. By employing a quantitative assessment, the primary objective is to develop methods for comprehensive evaluation of how such immersive technologies affect perceived visual quality, presence, engagement, and overall satisfaction and preference compared to traditional video communication techniques. The experimental design incorporates a series of tests where participants interact through state-of-the-art immersive communication setups, followed by detailed feedback sessions to gauge their experiences. Through this approach, the study seeks to uncover the nuances of user satisfaction in immersive environments and identify the key factors that enhance the overall quality of peer-to-peer communication.

关键词： quality of experience immersive communication subjective quality evaluation

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Underwater visual image denoising based on total variation method

Underwater visual image denoising based on total variation m...

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2022 conference on Infrared, Millimeter, Terahertz Waves and applications, IMT 2022

作者： Li, Yangyang He, Dahua Li, Yapeng Huazhong Institute of Electro-Optics Wuhan National Laboratory for Optoelectronics Wuhan430223 China

ISBN: (纸本)9781510662476

Optical imaging technology is one of the main technology for underwater detection and recognition. However, the serious image noise occurred in the optical imaging and the signal to noise ratio of image decreased, due to water scattering and turbulence, background light interference and other factors. In this paper, an underwater visual image denoising algorithm based on total variation method is proposed to improve the signal-to-noise ratio of the underwater image at the same time keep the detail information of images. Based on the linear noise model of the image, the total variation model of the image is established and the partial differential equation of image problem is converted to optimization problems, which can be solved by gradient descent algorithm and finite difference algorithm. The method based on total variation was proposed to reduce the noise of underwater images compare with other traditional algorithms and verified by the simulation experiment and underwater experiment. Experimental verifications: after the image processing of the algorithm, the objective reference image quality evaluation index image peak signal-to-noise ratio (PSNR) was improved to more than 20 dB, and the score of no reference image quality evaluation index was improved by 4.8 times. The method can be applied to an underwater exploration and image enhancement for scatterer imaging applications. © 2023 SPIE.

关键词： signal to noise ratio

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Rumors detection on x using pre-trained language models 8

Rumors detection on X using pre-trained language models

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8th IEEE International conference on image and signal processing and their applications, ISPA 2024

作者： Meghatria, Riadh Belhenniche, Hadjer Ferrah, Safa Djilali Bounaama University Khemis Miliana Algeria

ISBN: (纸本)9798350309249

With the rapid proliferation of internet technology, social media platforms, particularly microblogging sites like x (ex Twitter), have emerged as essential channels for news dissemination. However, the widespread dissemination of fake news and rumors has largely overshadowed this growth, primarily due to the absence of stringent oversight. While early research strategies employed traditional machine learning to address this challenge, the sheer volume of data in recent times has made deep learning methods increasingly *** this study, we introduce a retrospective rumor detection model that harnesses the capabilities of fine-tuned pre-trained language models. Our primary model achieved an F1-score of 92.14%. For comparison, we also developed a model that combines a pre-trained language model with a Logistic Regression classifier, obtaining an F1-score of 91.94%. In another model, we evaluated combined pre-trained language models for feature extraction with a Multi-Layer Perceptron (MLP) for classification and registered an F1-score of 92.28%. Intriguingly, our results underscore the potential of straightforward classifiers that leverage rich vectors from pre-trained language models, showing they can rival or even outperform complex neural architectures in rumor detection tasks. This revelation points towards a more efficient and less resource-intensive strategy to tackle online misinformation. © 2024 IEEE.

关键词： Learning systems

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Research and implementation of image edge detection based on Db4 wavelet transform 3

Research and implementation of image edge detection based on...

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3rd International conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Li, Donghui Yuan, Wei Chen, xu Guan, xuehui Liu, Dayu Harbin Cambridge University China

ISBN: (纸本)9781510668522

Wavelet transform is a new time-frequency domain analysis tool after Fourier transform. Wavelet analysis has very important applications in image processing, including image compression, image denoising, image fusion, image decomposition and image enhancement and so on almost all stages of image processing. By comparing several edge detection methods and combining the multi-scale characteristics of wavelet transform, the approximate coefficient and high frequency coefficient of wavelet transform decomposition are processed, and an image edge detection algorithm based on wavelet transform is proposed. In the MATLAB environment, firstly, taking an image as an example, Roberts operator, Sobel operator, Prewitt operator and Canny operator are compared to verify that Canny operator has better performance in edge location. Then the db4 wavelet is applied to this image and the other two images, the image is decomposed and reconstructed, and the Canny operator is used for edge detection. The simulation results show that the edge details extracted by this method are more abundant. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Edge detection

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Triplet Network with Autoencoder for Remote sensing image scene recognition and classification

Triplet Network with Autoencoder for Remote sensing image sc...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Hu, Fangchun Dai, Yifeng Wu, Lie Anhui University Anhui China Chuzhou University Anhui China

ISBN: (纸本)9781510680425

Remote sensing image scene categorization is a crucial process that involves assigning semantic labels to images based on their content,which has wide-ranging applications in a number of *** inherent potential of deep neural networks to learn attractive features has substantially sped up progress in the field, resulting in considerable breakthroughs in remote sensing and scene classification powered by deep ***, the existing literature lacks a comprehensive evaluation of research findings in deep learning for remote sensing image scene categorization, despite the notable advancements in this *** CNN has shown remarkable efficacy in extracting image features, ranging there are persistent challenges that hinder its optimal performance in the domain of categorization of images from data collected from remote *** intrinsic attributes linked to remote sensing image data, such as intraclass variability and inter-class similarities, result in a notable decrease in the classification accuracy of the ***, the discernible impact of noise and interference further exacerbates limitations in CNN performance within this specific *** response to these challenges, this study introduces an innovative classification methodology that seamlessly integrates a triplet network and ***, the paper systematically conducts a rigorous comparative assessment, evaluating the performance of diverse image classification models across various remote sensing *** holistic approach aims to contribute to the evolving landscape of remote sensing image scene classification by addressing existing gaps and advancing our understanding of effective model architectures. © 2024 SPIE.

关键词： Deep neural networks

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M-IRRA: A multilingual model for Text-based Person Search

M-IRRA: A multilingual model for Text-based Person Search

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2024 Asia Pacific signal and Information processing Association Annual Summit and conference

作者： Hung-Phong Tran Thi-Hoai Phan Thuy-Binh Nguyen Thi-Ngoc-Diep Do Hong-Quan Nguyen Thanh-Hai Tran Hien-Thanh Duong Thi-Lan Le Hanoi Univ Sci & Technol Sch Elect & Elect Engn SigM Lab Hanoi Vietnam Acad People Secur Fac Informat Secur Hanoi Vietnam Univ Transport & Commun Fac Elect Elect Engn Hanoi Vietnam Viet Hung Ind Univ Fac Informat Technol Hanoi Vietnam Hanoi Univ Min & Geol Dept Informat Technol Hanoi Vietnam

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367331;9798350367348

Text-based person search (TBPS) is a crucial task in computer vision and natural language processing, with various applications such as security surveillance, social media analysis, and multimedia database management. Thanks to advancement in deep learning, TBPS has made significant progress in handling English queries. However, existing methods often struggle with multilingual queries due to the diverse linguistic structures, vocabularies, and cultural nuances. To overcome this challenge, in this paper, we introduce a multilingual model for text-based person search, named M-IRRA, built upon the IRRA (Cross-Modal Implicit Relation Reasoning and Aligning) model - proposed for text-based person search in English [1]. In the M-IRRA model, the sentence transformer Multilingual CLIP is utilized to extract the feature embedding from text. Then, a dense layer is added to match and align the features extracted from text and image. The proposed model can support queries in multiple languages, ensuring strong performance across various linguistic contexts. Extensive experiments are performed on 3000VnPersonSearch dataset for TBPS in Vietnamese and its derived version in English to show the effectiveness of the proposed framework on both monolingual and multilingual search scenarios. The results obtained for monolingual search in Vietnamese are 38.60%, 67.4% and 77.62% at at rank 1, 5 and 10 respectively, showing a significant improvement over the existing models.

关键词： Vocabulary Social networking (online) Surveillance Information processing Multimedia databases Feature extraction Transformers Natural language processing Multilingual Security

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Fusion of Deep Learning Models for Multi-View image Classification 32

Fusion of Deep Learning Models for Multi-View Image Classifi...

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conference on signal processing, Sensor/Information Fusion, and Target Recognition xxxII

作者： Maguire, Brian Seminerio, Eleanor US Army DEVCOM AC Picatinny Arsenal NJ 07806 USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

Advances in Convolutional Neural Networks (CNN) have demonstrated state of the art performance in the tasks of image classification and object detection over the past decade. While significant progress has been made in development of more efficient networks, the computational and memory requirements still exceed practical limits in many applications. Additionally, the pose variability in such applications requires even larger training datasets for the network to generalize to all possible scenarios. The goal of this work is to develop an architecture for fusion of multiple views of a single target to provide robust classification with a lightweight backbone network used across all agents. Motivated by approaches to ensemble learning, we demonstrate that multiple weak learners with computationally efficient networks can combine to enhance classification accuracy. Three methods of fusion are considered: decision fusion, feature fusion, and multi-scale feature fusion. A novel network architecture is developed and implemented for each approach then trained and evaluated using synthetic data. For the feature fusion models, a custom training scheme is developed to minimize classification error while maintaining a common feature extraction backbone across agents. This conforms to a distributed classification use case where each agent has no prior knowledge of its position relative to target. Finally, we discuss the requirements for shared data of each approach in the context of applications with limited communication bandwidth.

关键词： Deep Learning image Classification image Fusion

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Comprehensive implementation of TextCNN for enhanced collaboration between natural language processing and system recommendation

Comprehensive implementation of TextCNN for enhanced collabo...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： xu, xiaonan xu, Zheng Ling, Zhipeng Jin, Zhengyu Du, Shuqian Northern Arizona University Flagstaff United States Stevens Institute of Technology HobokenNJ United States University of Sydney Sydney Australia Univeristy of California Irvine IrvineCA United States Trine University Phoenix ArizonaAZ United States

ISBN: (纸本)9781510680425

Natural Language processing (NLP) constitutes a pivotal domain of artificial intelligence focused on enabling computers to comprehend, process, and generate human language. Text classification, a fundamental NLP task, aims to categorize text into predefined classes. In recent years, deep learning has emerged as a dominant force across various research domains and has become a staple technology within NLP, particularly in text classification tasks. Unlike numerical and visual data, text processing underscores the need for nuanced processing capabilities. Traditional text classification methodologies typically involve preprocessing textual data and annotating samples manually to derive effective feature representations for classification using classical machine learning algorithms. This paper delves into the current landscape of deep learning applications in NLP, specifically in three core areas: text representation, sequence modeling, and knowledge representation. Furthermore, it explores the advancements and synergies facilitated by natural language processing in the realm of text classification, while also addressing challenges posed by adversarial techniques in text generation, classification, and semantic parsing. An empirical investigation into text classification tasks demonstrates the efficacy of interactive integration training, particularly in tandem with TextCNN, underscoring the pivotal role of these advancements in augmenting and refining text classification methodologies. © 2024 SPIE.

关键词： Knowledge representation

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Research on ship recognition technology based on infrared image

Research on ship recognition technology based on infrared im...

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

作者： Qu, Jianjing Chen, Weiyi Luo, Yasong Naval University of Engineering College of Weapons Engineering Hubei Province Wuhan430033 China

ISBN: (纸本)9781510666351

Infrared ship target recognition technology can automatically detect, analyze and identify ship targets, which is suitable for various types of working environments. This paper takes ship target image as the research object, and measures the effect of existing infrared detection technology, traditional target detection technology and target detection technology based on depth learning through data comparison. For infrared detection and target recognition, its working principle is summarized, and its ability to quickly identify ship types in practical applications is verified. The application and development status of Sea area planning、 supervision and Military reconnaissance field are summarized, and the future development trend is prospected. © 2023 SPIE.

关键词： Deep learning

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