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Automated Denoising of Diabetic Retinopathy images for Enhanced Medical Diagnosis

Automated Denoising of Diabetic Retinopathy Images for Enhan...

引用

Applied Artificial Intelligence and Computing (ICAAIC), International conference on

作者： G.V. Suresh Gokavarapu Pavani Naga Pranathi Sri Krishna Sai Angidi Tumma Sai Dheeraj Goda Naga Venkata Snehitha Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering(Autonomous) Mylavaram India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Globally, diabetic retinopathy (DR) continues to be a major cause of vision impairment. Early diagnosis and detection of DR are essential to preventing blindness and vision loss. But the traditional manual method of screening retinal pictures takes a lot of time and is prone to human mistake. Using image processing to apply automated denoising techniques can increase image quality, make DR lesions more visible, and help with correct diagnosis. In order to improve the quality of images of diabetic retinopathy, this study investigates the ability of four image denoising filters: guided image filtering, Gaussian-bilateral filtering, Gabor filtering, and Haar Wavelet filtering. AGAR300 (ieee DataPort), Diabetic Retinopathy Detection, Stare, Indian Diabetic Retinopathy image Dataset, and APTOS 2019 Blindness Detection are the five datasets used to evaluate the efficacy of these filters. The findings demonstrate that noise in DR images may be efficiently reduced by all four filters. The one that shows the biggest overall improvement in lesion visibility and image quality among them is guided image filtering.

关键词： image quality Diabetic retinopathy Noise reduction visual impairment Blindness Feature extraction Retina

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Development of A Relaxation Support System Utilizing Stereophonic AR

Development of A Relaxation Support System Utilizing Stereop...

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2025 International conference on Computing, Networking and communications, ICNC 2025

作者： Otsuka, Tomoki Li, Da Siriaraya, Panote Nakajima, Shinsuke Kyoto Sangyo University Kyoto Japan Fukuoka University Fukuoka Japan Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798331520960

Given the high prevalence of stress and anxiety in today's society, there is an urgent need to explore effective methods to help people manage stress. This research aims to develop a relaxation support system using stereophonic augmented reality (AR), designed to help alleviate stress by recreating relaxing environments with immersive stereo soundscapes, including stories created from generative AI and environmental sounds while users are going for a walk. This paper presents a preliminary evaluation of the effectiveness of the proposed relaxation support method. © 2025 ieee.

关键词： Stereo image processing

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Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation

Multi-Scale Denoising in the Feature Space for Low-Light Ins...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Joanne Lin Nantheera Anantrasirichai David Bull Visual Information Laboratory University of Bristol Bristol United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast. In this paper, we propose an end-to-end solution to address this challenging task. Our proposed method implements weighted non-local blocks (wNLB) in the feature extractor. This integration enables an inherent denoising process at the feature level. As a result, our method eliminates the need for aligned ground truth images during training, thus supporting training on real-world low-light datasets. We introduce additional learnable weights at each layer in order to enhance the network’s adaptability to real-world noise characteristics, which affect different feature scales in different ways. Experimental results on several object detectors show that the proposed method outperforms the pre-trained networks with an Average Precision (AP) improvement of at least +7.6, with the introduction of wNLB further enhancing AP by upto +1.3.

关键词： Instance segmentation Training Head image color analysis Semantic segmentation Noise reduction Object detection Feature extraction Speech processing Photonics

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Color Constancy Based on Deep Residual Learning 4

Color Constancy Based on Deep Residual Learning

引用

4th ieee International conference on Electronics and Communication Engineering, ICECE 2021

作者： Yang, Mengyao Xie, Kai Li, Tong Yang, Zepeng Beijing Institute of Graphic Communication Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipment Beijing China

ISBN: (纸本)9781728194226

The purpose of color constancy algorithm is to eliminate the influence of illumination on the color of objects in the scene, so that the computer has the same color constancy ability as human visual *** order to further improve the accuracy and robustness of the color constancy algorithm, this paper proposes a illumination estimation method based on deep residual learning, which fully extracts the illumination feature information in the image by deepening the number of network layers, and uses the residual module to prevent over fitting of the network model, At the same time, the local illumination estimates are integrated to obtain the global illumination estimation of the whole *** experimental results on ColorChecker data set show that the estimation accuracy and robustness of this method are good, and can be applied to the fields of image processing and computer vision requiring color correction. ©2021 ieee

关键词： Network layers

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Disocclusion-Reducing Geometry for Multiple RGB-D Video Streams

Disocclusion-Reducing Geometry for Multiple RGB-D Video Stre...

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28th ieee conference on Virtual Reality and 3D User Interfaces (ieee VR)

作者： Lee, Jaesuk Kim, Youngwook Yun, Jehyeong Yun, Joungil Cheong, Won-Sik Ihm, Insung Sogang Univ Dept Comp Sci & Engn Seoul South Korea Elect & Telecommun Res Inst ETRI Gwangju South Korea

ISBN: (纸本)9781665440578

Depth-image-based rendering (DIBR) is a key method for synthesizing virtual views from multiple RGB-D video streams. A challenging issue inherent in this approach is the disocclusion that occurs as the virtual viewpoint moves away from a reference view. In this work, we present a technique for extracting 3D geometric data, called the disocclusion-reducing geometry, from the input video streams. This auxiliary information, represented as a 3D point cloud, can then be combined easily with a conventional DIBR pipeline to reduce the disoccluded region as much as possible during the view warping process, eventually decreasing the visual artifacts by the subsequent hole-filling process.

关键词： Computing methodologies Computer graphics image manipulation image-based-rendering

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Continuous Differential image-based Fast Convolution for Convolutional Neural Networks 13

Continuous Differential Image-based Fast Convolution for Con...

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13th International conference on Information and Communication Technology Convergence, ICTC 2022

作者： Hong, Sunghoon Park, Daejin School of Electronic and Electrical Engineering Kyungpook National University Daegu Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Convolutional neural networks with powerful visual image analysis of deep structures are gaining popularity in many research fields. The main difference in convolutional neural networks compared to other artificial neural networks is the addition of many convolutional layers. The convolutional layer improves the performance of artificial neural networks by extracting feature maps required for image classification. However, for applications that require very low-latency on limited processing resources, the success of a convolutional neural network depends on how fast we can compute. In this paper, we propose a novel convolution technique of fast algorithms for convolutional neural networks using continuous differential images. The proposed method improves the response speed of the algorithm by reducing the computational complexity of the convolutional layer. It is compatible with all types of convolutional neural networks, and the lower the difference in the continuous images, the better the performance. We use the darknet network to benchmark the CPU implementation of our algorithm and show state-of-the-art throughput at pixel difference thresholds from 0 to 25 pixels. © 2022 ieee.

关键词： Convolutional neural networks

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Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition

Col-OLHTR: A Novel Framework for Multimodal Online Handwritt...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chenyu Liu Jinshui Hu Baocai Yin Jia Pan Bing Yin Jun Du Qingfeng Liu University of Science and Technology of China Hefei China iFLYTEK Research Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder. However, these approaches face several drawbacks: 1) single encoders typically focus on either local trajectories or visual regions, lacking the ability to dynamically capture relevant global features in challenging cases; 2) multi-stream encoders, while more comprehensive, suffer from complex structures and increased inference costs. To tackle this, we propose a Collaborative learning-based OLHTR framework, called Col-OLHTR, that learns multimodal features during training while maintaining a single-stream inference process. Col-OLHTR consists of a trajectory encoder, a Point-to-Spatial Alignment (P2SA) module, and an attention-based decoder. The P2SA module is designed to learn image-level spatial features through trajectory-encoded features and 2D rotary position embeddings. During training, an additional image-stream encoder-decoder is collaboratively trained to provide supervision for P2SA features. At inference, the extra streams are discarded, and only the P2SA module is used and merged before the decoder, simplifying the process while preserving high performance. Extensive experimental results on several OLHTR benchmarks demonstrate the state-of-the-art (SOTA) performance, proving the effectiveness and robustness of our design.

关键词： Training Handwriting recognition visualization image recognition Text recognition Speech recognition Trajectory Decoding Speech processing Streams

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Beam Hopping for LEO Satellite:Challenges and Opportunities

Beam Hopping for LEO Satellite:Challenges and Opportunities

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2022 International conference on Culture-Oriented Science and Technology, CoST 2022

作者： Zhao, Qi Hu, Yiqing Pang, Zhenyu Ren, Derong China Academy of Space Technology Beijing Institute of Spacecraft Environment Engineering Beijing China Southeast University National Nobile Communications Research Laboratory Nanjing China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

Low earth orbit (LEO) satellite networks can provide global broadband access services and thus are complementary to current terrestrial mobile communication systems. The limited resources of LEO satellite platform and the uneven distribution of ground services bring many challenges to the design of satellite ground access scheme. The beam hopping (BH) technology meets the non-uniform and dynamic service requirements of users through time slicing technology, and enhances the resource utilization and service capability of satellites. However, the application of BH technology in LEO satellites is facing many challenges. This paper analyzes and considers from the system and technical aspects, combs and analyzes its research work. Finally, the development trend of BH system is given, which provides an idea for the application and development of BH technology for LEO satellites. © 2022 ieee.

关键词： Orbits

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A NO-REFERENCE visual QUALITY METRIC FOR 3D COLOR MESHES

A NO-REFERENCE VISUAL QUALITY METRIC FOR 3D COLOR MESHES

引用

ieee International conference on Multimedia and Expo (ICME)

作者： Zhang, Zicheng Sun, Wei Min, Xiongkuo Wang, Tao Lu, Wei Zhu, Wenhan Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665449892

To improve the viewers' quality of experience (QoE) in computer graphics applications, the visual quality assessment (VQA) of 3D meshes is becoming a popular task in the multimedia area. Since 3D meshes are quite sensitive to the processing operations like simplification and compression, many studies concerning the VQA of 3D meshes have been carried out to measure the caused degradations. However, the previous studies mostly utilize full-reference metrics and focus mainly on the geometry attributes. While in some application scenarios such as 3D reconstruction, digital entertainment, and medical modeling, the reference 3D mesh is not always available and the color information can not be ignored. Therefore, in this paper, we propose a no-reference visual quality metric for 3D color meshes, which is based on the statistical parameters and entropy estimated from the probability distributions of curvature, dihedral angles, face area, face angle, and diffuse color information. The performance of the proposed method is validated on a database specially built for VQA of 3D color meshes. Experimental results show that our metric operates stably and achieves the highest correlation with subjective judgement.

关键词： visual quality assessment (VQA) noreference (NR) 3D meshes diffuse color

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Weather Classification Based on Convolutional Neural Networks 3

Weather Classification Based on Convolutional Neural Network...

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3rd International conference on Wireless communications and Smart Grid, ICWCSG 2021

作者： Zhao, Xinyu Wu, Changhao Anhui University School of Electronics and Information Engineering Hefei230601 China

ISBN: (纸本)9781665425988

Identification and forecast of weather conditions are important for transportation safety, environment, meteorology. Under the background of Artificial intelligence, the methods of weather conditions recognition based on deep learning can solve the problems of traditional weather conditions recognition. In addition, it can also realize the real-time judgment of weather recognition. Convolutional Neural Network (CNN) is an important network structure in deep learning. By introducing convolution layer, pooling layer, and deeper network structure, CNN perceives higher semantic features and enhances the image classification effect. In view of the weather conditions of visual images (sunny, foggy, rainy, snowy) which are difficult to be identified by the traditional weather recognition methods. In this paper, we proposed a weather recognition framework based on the convolution neural network architecture whose accuracy is improved by 5.06% compared with traditional methods. © 2021 ieee.

关键词： image classification

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