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3D image visual Communication Optimization System on Account of image processing Technology

3D Image Visual Communication Optimization System on Account...

引用

Mobile Networks and Wireless communications (ICMNWC), IEEE International conference on

作者： Hongbo Sun Changchun Humanities and Sciences College Changchun Jilin China

ISBN: (纸本)9781665491129

As an efficient tool in the new era, computer plays a great role in image processing, video production and development. In the 21st century, the application field of image technology is more extensive, and image technology has also become an important direction of intelligent development. The research on image technology no longer stays in the laboratory. More and more enterprises have also joined the research and application of image technology, and achieved certain results in the fields of object recognition, target tracking, work piece detection and so on. This paper studies the content and knowledge of the design of 3D image visual communication optimization system based on image processing technology, explains the process and technology of film and television image processing and optimization, and expounds the process of system optimization and data analysis in detail. Through the effect analysis of data samples, the test effect shows that with the help of image processing technology, this system has good processing effect in the field of video and image, and has professional processing effect in many aspects of image and video.

关键词： Wireless communication Three-dimensional displays visual communication image processing Symbols Input devices Blockchains

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Design and Implementation of image Recognition System Based on AI Intelligent Video Technology

Design and Implementation of Image Recognition System Based ...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International conference on

作者： Qingrong Huang China Mobile Communications Group Fujian Co. Ltd. Fuzhou Fujian China

Based on the research of AI (Artificial Intelligence) technology and image recognition technology, this article designed a monitoring and recognition system around the principle of universality. image data collection is achieved by using external devices such as scanners to digitize and store data; image preprocessing is the enhancement processing of scanned images to achieve a certain degree of visual change to meet certain requirements. The main work includes: image gray correction (histogram equalization, linear and nonlinear transformation of histogram), image noise reduction, and image tilt correction; the image segmentation and recognition part can achieve dynamic cutting of images, and extract images of specific regions, thus analyzing and processing them to obtain accurate information. In image recognition rate monitoring, the recognition rate of personnel leaving the monitoring range without permission was 94%. The system used in this article met the predetermined requirements of the design.

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visual Analysis Motivated Super-Resolution Model for image Reconstruction

Visual Analysis Motivated Super-Resolution Model for Image R...

引用

IEEE visual communications and image processing (VCIP)

作者： Huifen Wang Junda Xue Mingchuan Yang Yuan Zhang College of Big Data and Artificial Intelligence China Telecom Research Institute Shanghai China College of Big Data and Artificial Intelligence China Telecom Research Institute Beijing China College of Information Science and Electronic Engineering Zhejiang University College of Big Data and Artificial Intelligence China Telecom Research Institute Hangzhou China

ISBN: (纸本)9781665475938

This paper presents a concise end-to-end visual analysis motivated super-resolution model VASR for image reconstruction. Compatible with the existing machine vision feature coding framework, the features extracted from the machine vision task model are super-resolution amplified to reconstruct the original image for human vision. The experimental results show that without additional bit-streams, VASR can well complete the task of image reconstruction based on the extracted machine features, and has achieved good results on COCO, Openimages, TVD, and DIV2K datasets.

关键词： Training visualization Analytical models image coding visual communication Machine vision Superresolution

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Leveraging visual Language Information for Enhanced Person Re-Identification: A Robust Framework for Real-World Applications

Leveraging Visual Language Information for Enhanced Person R...

引用

2025 IEEE International conference on Consumer Electronics, ICCE 2025

作者： Han, Seunghee Jeong, Dasol Park, Hasil Park, Jiwon Lee, Hyebean Paik, Joonki Department of Image Chung-Ang University Seoul Korea Republic of Department of Artificial Intelligence Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9798331521165

Recently, performance improvements through vision-language learning have gained significant attention in the field of image re-identification (ReID). In particular, the CLIP-ReID [1] achieved enhanced visual representations by introducing learnable text token sets for each identity. However, existing approaches have not fully exploited fine-grained part-specific features of the human body, which are crucial when applying person re-identification technology in real-world scenarios where partial occlusions or other factors might affect accuracy. To address this limitation, we propose a novel framework that incorporates body part-specific learnable text tokens, leveraging visual language information related to specific body regions. This approach is essential for mitigating the impact of occlusions and enhancing the robustness of ReID systems in practical settings. The process of applying visual language information through CLIP-ReID involves several stages. First, body part regions are identified and segmented within the image. Next, visual features specific to these regions are extracted and mapped to corresponding text tokens that describe these features in natural language. Finally, these text tokens are integrated with the image features to create a more comprehensive representation that captures both visual and semantic aspects, improving the discrimination of individuals based on specific anatomical features. Experimental results not only using the MSMT17 [2] and Market-1501 [3] datasets but also from a real-world scenario involving 11 CCTV cameras installed on the campus of Chung-Ang University demonstrated that the proposed method significantly outperformed existing models. This confirms the effectiveness of vision-language learning that utilizes segmented body part information in the image re-identification task. © 2025 IEEE.

关键词： Text processing

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No-reference Stereoscopic image Quality Assessment Based on Parallel Multi-scale Perception

No-reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE visual communications and image processing (VCIP)

作者： Ziyi Zhang Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781665475938

With the rapid development of 3D technologies, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods are in great demand. In this paper, we propose a parallel multi-scale feature extraction convolution neural network (CNN) model combined with novel binocular feature interaction consistent with human visual system (HVS). In order to simulate the characteristics of HVS sensing multi-scale information at the same time, parallel multi-scale feature extraction module (PMSFM) followed by compensation information is proposed. And modified convolutional block attention module (MCBAM) with less computational complexity is designed to generate visual attention maps for the multi-scale features extracted by the PMSFM. In addition, we employ cross-stacked strategy for multi-level binocular fusion maps and binocular disparity maps to simulate the hierarchical perception characteristics of HVS. Experimental results show that our method is superior to the state-of-the-art metrics and achieves an excellent performance.

关键词： image quality visualization Three-dimensional displays visual communication Stereo image processing Neural networks visual systems

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Flicker-Free Adaptive Enhancement for Infrared Video 13

Flicker-Free Adaptive Enhancement for Infrared Video

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Wei, Caixia Gan, Zongliang Nanjing University of Posts and Telecommunications Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing China

ISBN: (纸本)9781665407854

Infrared videos often suffer from low contrast and insufficient texture. In order to improve poor visual effect, a novel infrared video enhancement method is proposed, which contains intra-frame brightness enhancement and inter-frame brightness continuity adjustment. In intra-frame brightness enhancement, it has two steps including global contrast enhancement and local details enhancement. To global contrast enhancement, the processed image contrast by geometric distribution, and it is incorporated into an optimization problem with a sorted histogram of each frame to yield a modified histogram. To local details enhancement, after Retinex decomposition, the reflection component is magnified by boundary preserving filter, and this strategy can effectively suppress noise in relatively uniform regions. Furthermore, to maintain inter-frame brightness continuity, adaptive parameters are introduced through optical flow to control the combination ratio of geometric distribution histogram, in which the enhancement degree of each frame can be adjusted according to inter-frame similarity and the moving region are enhanced adaptively according to the change of each frame. Experimental results demonstrate that proposed algorithm can effectively enhance the contrast and detail of infrared video, and improve the visual effect in subjective and objective quality evaluation. © 2021 IEEE.

关键词： Luminance

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image Forgery Detection Using Hybrid Model

Image Forgery Detection Using Hybrid Model

引用

Intelligent Signal processing and Effective Communication Technologies (INSPECT), IEEE International conference on

作者： Priya Metri Sakshi Balla Shreya Pawar Atharva Sharma Prashant Sakunde Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The growing demand for trustworthy picture forgery detection techniques to maintain the integrity of visual content across a range of applications is addressed in this work. To improve picture authentication accuracy and efficiency, a user-friendly cascaded framework combining Support Vector Machines (SVM) and VGG 16 is presented. First, a highly effective supervised learning system called Support Vector Machines (SVM) rapidly filters photos by identifying overt alteration indi-cators. After passing this phase, images are examined and refined even more with the help of the VGG 16 deep learning model. When SVM and VGG16 are combined, their complimentary strengths are utilised, which improves accuracy and speeds up identification when compared to other approaches. According to benchmark datasets, the proposed method outperforms earlier methods with respect to accuracy and computational efficiency. This strategy may benefit many real-world applications- based on forensic verification, verification of media integrity, medical image authentication, and intellectual property protection-in the here-and-now solutions. This work enhances the confidence and trustworthiness of digital communications by providing an efficient, friendly user tool for the verification of visual content.

关键词： Support vector machines visualization Accuracy Filters Forensics Supervised learning Authentication Intellectual property Media Forgery

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DNAT: Multi-scale Transformer with Dilated Neighborhood Attention for image Classification

DNAT: Multi-scale Transformer with Dilated Neighborhood Atte...

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International conference on Wireless communications and Signal processing (WCSP)

作者： Chenfeng Jiang Quan Zhou Zhiyi Mo Jing Wang Yawen Fan Xiaofu Wu Suofei Zhang Bin Kang National Engineering Research Center of Communications and Networking Nanjing University of Posts & Telecommunications P.R. China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China Department of Internet of Things Nanjing University of Posts & Telecommunications P.R. China

Recently, Transformer-based models have achieved remarkable progress for image classification. In order to reduce the computational costs of multi-head self-attention (MHSA) that is widely adopted in recently-proposed visual Transformers, some window-based Transformers employ local self-attention via shrinking number of input tokens. Nevertheless, these approaches to some extent are short to model long-range dependencies. To address this problem, this paper presents an effective and powerful Transformer backbone, called Dilated Neighborhood Attention Transformer (DNAT) that has a mutli-path architecture, for image classification. DNAT is able to explore long-range interactions through dilated neighborhood attention (DNA). When dilation rate is small, DNAT degenerates to local self-attention that is computationally efficient to capture local features. As the dilation rate increases, more and more long-range token interactions are explored step-by-step. Finally, by integrating weighted features derived from various dilation rates, DNAT seamlessly harvests local-to-global context, yet with acceptable growth of model size and computational budgets. The extensive experiments on imageNet-1K and CIFAR100 datasets demonstrate the effectiveness of DNAT for image classification.

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A Scale-Arbitrary Network for Chinese Painting Super-Resolution 23

A Scale-Arbitrary Network for Chinese Painting Super-Resolut...

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6th International conference on Information Science and Systems, ICISS 2023

作者： Wu, Yuekun Zhou, Yue Xu, Kun State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798400708206

image super-resolution (SR) plays a crucial role in enhancing low-resolution (LR) images to high-resolution (HR) images. In the context of Chinese painting, SR is of great significance for cultural presentation and post-processing. However, existing methods for super-resolution are limited to specific integer scale factors, and when it comes to the super-resolution of Chinese painting with large scales, the preservation of intricate textural details is compromised. In this paper, we propose a novel super-resolution network capable of handling arbitrary scale factors and preserving rich textural details for Chinese painting. Our network integrates the Meta-RCAN framework with a Scale-Aware Feature Learning Module to achieve superior effects. Furthermore, we introduce Haar loss and degradation loss to better preserve high-frequency features and enhance realistic details and textures. We also introduce global residual learning and fusion to obtain more shallow features. To facilitate our research, we create a large-scale high-performance dataset, Chi-Paint-HD, specifically designed for Chinese painting images. Experimental results demonstrate that our model achieves superior super-resolution performance for arbitrary scale factors, outperforming others models in terms of peak signal-To-noise ratio (PSNR), structural similarity (SSIM), and overcoming over-smoothing issues. Additionally, our model achieves excellent quantitative evaluation results and high-quality visual performance for integer scale factors, surpassing previous models that are limited to specific scales. © 2023 ACM.

关键词： Textures

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ACIGS: An automated large-scale crops image generation system based on large visual language multi-modal models

ACIGS: An automated large-scale crops image generation syste...

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IEEE communications Society conference on Sensor and Ad Hoc communications and Networks (SECON)

作者： Bolong Liu Hao Zhang Jie Liu Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science and Technology Harbin Institute of Technology (ShenZhen) ShenZhen China

Smart agriculture requires an extensive convergence of information technology and agriculture. Attaining intelligence mandates an enormous amount of data to train models. However, it is challenging to acquire a large number of crop image data, limiting the application and growth of computer vision technology in agriculture. To address this problem, we designed a crop image generation system that combines a large language model with visual language multi-modal large models to augment the scale, variety, and resolution of crop image data. First, the system inputs existing real crop images into the visual language multimodal model to extract features and represent crop images in text form. Then, the system passes the crop text representation to the language model for cleaning and processing, which generates prompts to create crop images. The prompts are input into the visual language multi-modal model to generate crop images based on text representation of crops. The resulting crop images undergo image quality evaluation in the visual language multimodal model, and high-quality crop images are saved to the crop image dataset based on the quality evaluation. These steps lead to the formation of the final generated crop image dataset. The experimental results indicate that the crop images generated using the proposed system are similar to but different from the example images. This characteristic enables the expansion of crop data while circumventing redundancy and allowing for resolution control, which is crucial for dense segmentation tasks. Using this method, the existing data can be enlarged up to 7.5 times.

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