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Enhancing Satellite image Resolution Using Super Resolution Techniques 2nd

Enhancing Satellite Image Resolution Using Super Resolution ...

引用

2nd International conference on Applied Intelligence, ICAI 2024

作者： Berezovsky, Vladimir Kamenev, Alexander Sharshov, Ivan Shoshina, Kseniya Vasendina, Irina Aleshko, Roman Desyatova, Tatyana Federal University Arkhangelsk163002 Russia

ISBN: (纸本)9789819619030

This study investigates various super-resolution (SR) methods for enhancing the spatial resolution of satellite imagery. Traditional interpolation techniques, such as nearest-neighbor, bilinear, bicubic, and Lanczos, are compared with modern deep learning-based models, including EDSR, ESPCN, FSRCNN, and LapSRN. The research demonstrates that deep learning approaches outperform traditional methods in terms of image quality, as indicated by higher PSNR and SSIM values. Additionally, the integration of geographic information systems (GIS) data into the SRGAN framework is explored, further improving image reconstruction by processing collateral data with residual scaling in RRDB blocks. The results show that combining advanced SR methods with optimized computational resources provides significant improvements in satellite image resolution, making it highly relevant for environmental monitoring, urban planning, and disaster management. Future research should focus on optimizing these models for specific applications and exploring emerging technologies for further enhancements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Satellite imagery

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A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology 10th

A 3D MRI Brain Image Segmentation and Reconstruction System ...

引用

10th China Health Information processing conference, CHIP 2024

作者： Zhao, Wang Lu, Peixin Hu, LianTing Lu, Long School of Management Engineering Henan University of Engineering Zhengzhou China School of Safety Science and Emergency Management Wuhan University of Technology Wuhan China Guangdong Provincial People’s Hospital Guangzhou China School of Information Management Wuhan University Wuhan China The Center for Healthcare Big Data Research The Big Data Institute Wuhan University Wuhan China Institute of Pediatrics Guangzhou Women and Children’s Medical Center Guangzhou Medical University Guangzhou China School of Public Health Wuhan University Wuhan China

ISBN: (纸本)9789819637546

Background With the rapid development of information technology and the digitization of medical devices, various diseases require the use of medical imaging equipment for diagnosis. At present, various medical imaging diagnostic equipment such as CT and nuclear magnetic resonance can provide two-dimensional planar images of diseases. Doctors urgently need to accurately determine the spatial location, size, geometry, and spatial relationship with the surrounding tissue. Therefore, it is very important to use computer technology to segment 3D MRI images, determine the location of lesions, and then perform 3D reconstruction. Method At present, automatic recognition and marking of brain images are displayed in two dimensions. Therefore, it is necessary to use 3D visualization technology for reconstruction. In addition, it can be combined with virtual and real, and some additional information is superimposed on the brain image for integrated display. In addition, a combination of virtual and real needs to be superimposed, and some additional information is superimposed on the brain image for integrated display. The research focus of this paper includes two main parts: disease segmentation and 3D reconstruction visualization. Firstly, the disease segmentation method based on 3D MRI brain image files was designed, and then the feature extraction and 3D reconstruction functions were designed. Thereby forming a complete process of disease region segmentation and three-dimensional reconstruction. Results This study is based on a three-dimensional MRI brain image segmentation algorithm. The algorithm is advanced in technology, high in accuracy, and can effectively identify the location of the disease. Then, this study used the Unity tool to implement a three-dimensional reconstruction and visual display program for brain image disease segmentation. Therefore, the doctor can quickly and intuitively grasp the spatial information inside the brain and the information of the lesion

关键词： image segmentation

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Enhanced image Deblurring with the Fusion of Generative Adversarial Networks and Transformer Models 1st

Enhanced Image Deblurring with the Fusion of Generative Adve...

引用

1st International conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Ranjan, Arti Ravinder, M. New Delhi India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031808418

In the domain of image processing, deblurring remains a challenging task, particularly in restoring high-fidelity details from blurred images. This paper introduces a novel approach that synergizes the strengths of Generative Adversarial Networks (GANs) and Transformer models to address this challenge. Our methodology leverages the generative capability of GANs to produce realistic and sharp images, while incorporating the Transformer’s prowess in capturing long-range dependencies and intricate details within images. This adversarial training regime is designed to refine the model’s ability to generate deblurred images that are not only accurate in detail but also high in perceptual quality. Through extensive experiments on various datasets, our model demonstrates superior performance in deblurring, outperforming existing state-of-the-art methods in terms of both objective metrics and subjective visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection 1st

Composite Awareness-Based Knowledge Distillation for Medical...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Dong, Shiwen Ding, Xiaowei Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

In the field of medical image analysis, supervised deep learning methods have become mainstream but still face significant challenges. Firstly, they require substantial annotated data, which is costly and time-consuming to obtain. Secondly, there is a class imbalance issue, especially with rare diseases that have limited images and diverse lesion types, making accurate modeling difficult. To address these issues, we propose an anomaly detection model based on knowledge distillation. Our approach leverages the differences in intermediate layer features between a pre-trained teacher network and a student network trained on normal images through knowledge distillation to detect anomalies. We incorporate a composite awareness module to learn both local regional and global spatial information effectively. Experiments conducted on three medical datasets prove our approach performs competitive anomaly detection results compared to existing methods for medical images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical image processing

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Stock Investor Sentiment Analysis Based on NLP 1st

Stock Investor Sentiment Analysis Based on NLP

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Jingxin Deng, Chao Yang, Jie Shanghai Normal University Tianhua College AI School Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

This study investigates the reciprocal relationship between investor sentiment and capital market dynamics, leveraging advanced techniques in data extraction and analysis. By employing web crawling and natural language processing (NLP), we gather and categorize investor comments from stock forums pertinent to the Shanghai and Shenzhen Indices. Utilizing Support Vector Machines and Logistic Regression within an NLP framework, we develop an automated system that transforms comments into sentiment indicators. These indicators are further quantified into daily sentiment indices following denoising procedures to emphasize persistent market trends. Comparative assessments reveal strong correlations between refined sentiment indices and actual stock index movements, confirming their predictive power for market fluctuations. The study offers investors a sentiment-analytics tool, augmenting the multi-faceted approach to investment strategy formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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Underwater image Enhancement Method Based on Contrast Stretching and Lab Color Space Correction 2nd

Underwater Image Enhancement Method Based on Contrast Stretc...

引用

2nd International conference on Applied Intelligence, ICAI 2024

作者： Chen, Jianlei Wang, Zuheng Hu, Jun Wang, Quanyu Chen, Guanyu School of Computer Science China University of Geosciences Wuhan430074 China Informatization Office China University of Geosciences Wuhan430074 China China Mobile Group Design Institute Co. Ltd. Beijing100089 China

ISBN: (纸本)9789819619030

Enhancing underwater images has been a widely discussed topic. To address issues such as color distortion due to wavelength attenuation and brightness/contrast distortion caused by suspended particles, many recent methods have been developed. This paper proposes an underwater image enhancement method that comprehensively considers various degradation factors. In the Lab color space, the paper applies Contrast-Limited Adaptive Histogram Equalization combined with normalization and gamma correction techniques to further optimize the image's brightness and contrast. Linear contrast stretching is also applied to significantly improve the overall contrast, ensuring color fidelity while avoiding over-enhancement. Experimental results demonstrate that this method outperforms six state-of-the-art algorithms across two datasets, leading to significant improvements in visual effects and detail clarity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Underwater imaging

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ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation 1st

ECLNet: Efficient Convolution with Lite Transformer for 3D M...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Zheng, Lei Zhang, Yuzhong Chen, Huagui Guo, Yuheng Zhou, Lei School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Attrsense Shangha Shanghai China Shanghai Waigaoqiao Shipbuilding and Offshore University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9789819633487

Convolutional neural networks (CNNs) have been significantly advanced in the field of medical imaging in recent years. The implementation of U-shaped architectures has proven particularly effective, demonstrating exceptional results across various image segmentation challenges. Despite these achievements, convolutional operations face challenges in capturing broad-scale global context and long-distance semantic relationships. Therefore, the ECLNet is presented in this paper, a converter with an architecture similar to U-Net that was specifically designed for medical image segmentation. With the help of the Transformer architecture, complete global context is extracted by this model through the processing of feature maps produced by the CNN as input sequences. To achieve accurate feature localization, the CNN feature map is combined with the global context information. It is claimed that the Transformer design can be utilized as a reliable encoder for medical image segmentation applications. Its combination with convolutional networks enhances segmentation efficiency by facilitating the restoration of local spatial information. Finally, extensive experiments on one public and one private datasets show that our proposed algorithm performs better than several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Edge-IoT Architecture-Based visualization Service Platform for RTO Real-Time Monitoring

Edge-IoT Architecture-Based Visualization Service Platform f...

引用

International conference on Advanced Communication Technology (ICACT)

作者： Hoon-Min Park Dal-Hwan Yoon Hyun-Min Jung Dae-Hee Lee Tae-Yeung Lim Hyun Kim Myung-Ki Jang Emsolution Co. Ltd Suwon Korea Department of Electronic Engineering Semyung University Korea Devicenet Co. Ltd Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

In this study, we present the Edge IoT Architecture (Architecture) for analyzing the real-time state information of the regenerative thermal oxidizer (RTO) and implement a visualization method. The Edge-IoT architecture system interfaces with the programmable logic control (PLC) that controls the monitoring of RTO operations and transmits real-time data to a remote server, analyzes the channel data received through the IoT to build a database, and visualizes the characteristics of database (DB) analysis. IoT architecture implementation for fault diagnosis and maintenance of RTO designs IoT circuits on PLC control panels that control monitoring. Compatibility and information security are considered to facilitate remote maintenance. The received data is segmented by each channel and analyzed by a visualization algorithm to detect anomalies in the segmented data. At this time, the visualized data is judged to be normal, insufficient, or warning, depending on the threshold.

关键词： image edge detection Data visualization Signal processing algorithms Signal processing Real-time systems Maintenance Thermal analysis visual databases Servers Monitoring

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Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis 1st

Application of Classification Framework Based on CDR and CNN...

引用

1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Xu, Yunjiao Yang, Jie Guo, Tianjiao Scientific Research Office Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

The seven-field classification plays a key role in ophthalmology, especially in the diagnosis and treatment of diabetic retinopathy. In this paper, we designed a framework that can classify 7 fields of each eye simultaneously, and applied it to the medical-assisted interactive system of 7 fields of classification。F1 and F2 field of view are detected through a combination of algorithms based on decision rules and convolutional neural networks. The total accuracy on the internal test set is 98.7%(95%CI, 98.4%–99.1%). In external tests, the total accuracy of the algorithm in this paper on the LONGITUDINAL data set is 98.6%(95%CI,97.9%–99.3%). The total accuracy on the Drishti-GS data set is 100%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Family Doctor Model Training Based on Large Language Model Tuning 1st

Family Doctor Model Training Based on Large Language Model T...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Zhu, Yu Li, Wangtao Ling, Xufeng Yang, Jie Shanghai China School of AI Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

As society increasingly focuses on health issues, the importance of the role of family doctors becomes more apparent. The question of how to use cutting-edge technology to improve the accessibility and efficiency of medical services is a critical topic. Generative large language models, with their excellent capabilities in natural language processing, offer new possibilities for the implementation of intelligent family doctor systems. This study focuses on the application of generative large language models in family doctor systems, selecting the ChatGLM2-6B model as the base model for this research. Through a custom-written data collection program, a dataset containing 170,000 high-quality medical question-and-answer entries was constructed. The LoRA method was used to tune the base model, and the performance of the original and tuned models in medical question-and-answer tasks was examined. The practical results of this study show that the generatively tuned large language model has achieved a 70% improvement in ROUGE scores compared to the untuned model, validating the effectiveness of large language model tuning in the family doctor field. Furthermore, this study has open-sourced the collected dataset and program, providing a technical reference for deploying efficient and accurate question-and-answer systems in similar vertical fields in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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