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Multi-information Aggregation Network for Fundus image Quality Assessment

Multi-information Aggregation Network for Fundus Image Quali...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuan Li Guanghui Yue Lvyin Duan Honglv Wu Tianfu Wang Health Science Center School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665475938

Fundus image quality assessment (IQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists. Existing fundus IQA methods mainly explore local information to consider local distortions from convolutional neural networks (CNNs), yet ignoring global distortions. In this paper, we propose a novel multi-information aggregation network, termed MA-Net, for fundus IQA by extracting both local and global information. Specifically, MA-Net adopts an asymmetric dual-branch structure. For an input image, it uses the ResNet50 and vision transformer (ViT) to obtain the local and global representations from the upper and lower branches, respectively. In addition, MA-Net separately feed different images into the two branches to rank their quality for supplementing the feature representations. Thanks to the exploration of intra- and inter-class information between images, our MA-Net is competent for the fundus IQA task. Experiment results on the EyeQ dataset show that our MA-Net outperforms the baselines (i.e., ResNet50 and ViT) by 3.06% and 7.61% in Acc, and is superior to the mainstream methods.

关键词： image quality visual communication Semantics Feature extraction Transformers Distortion Convolutional neural networks

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Dual-modal Fusion Method based on Hierarchical Frequency Domain Neural Network

Dual-modal Fusion Method based on Hierarchical Frequency Dom...

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Information Systems and Computing Technology (ISCTech), International conference on

作者： Guoxia Xu Yue He Lizhen Deng Hu Zhu School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

To address the challenges of image degradation and the limitations of single-domain information, this paper proposes a fusion network that integrates both frequency-domain and spatial-domain information for infrared and visible image fusion. We begin by designing an image fusion network that combines the phase components of the visible image with its amplitude spectrum, effectively processing the high, low and mid-frequency. This method achieves the fusion of salient targets and the preservation of visual quality in the frequency domain. Additionally, an information complementarity network is introduced to address spatial domain differences. This network features an innovative compensation mechanism that addresses the texture disparities between the fused image. Furthermore, the network utilizes implicit degradation estimation during feature fusion. This approach helps the fusion model identify and optimize various potential degradation factors, thereby improving the extraction and aggregation of important content in degraded scenes. The experiment demonstrates our proposed method achieves the superior performance against other comparison methods.

关键词： Degradation Computers visualization Frequency-domain analysis Neural networks Estimation Feature extraction Sensors image fusion Information systems

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Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for Criminal Investigation images 4

Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for...

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4th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Lan, Rong Gao, Xiaoge School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Criminal investigation image segmentation is considered one of the most important tasks in the field of criminal investigation image processing. However, the segmentation for criminal investigation images is a challenging task due to natural or human factors. In this paper, an image threshold segmentation algorithm based on fuzzy Kaniadakis entropy was proposed for criminal investigation image segmentation. Firstly, the weighted least squares filter was used for image pre-processing. Then, by using membership functions, image fuzzy sets were constructed based on restricted dissimilarity function. Finally, the maximum fuzzy Kaniadakis entropy was corresponded to the optimal segmentation threshold. The experimental results demonstrate that compared with several existing entropy-based thresholding algorithms, the proposed algorithm is effective in terms of visual effects and segmentation quality measures. © 2021 ACM.

关键词： image segmentation

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A Multi-User Oriented Live Free-Viewpoint Video Streaming System Based on View Interpolation

A Multi-User Oriented Live Free-Viewpoint Video Streaming Sy...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Hu, Jingchuan Guo, Shuai Dong, Yu Zhou, Kai Xu, Jun Song, Li Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

As an important application form of immersive multimedia services, free-viewpoint video (FVV) enables users with great immersive experience by strong interaction. However, the computational complexity of virtual view synthesis algorithms poses a significant challenge to the real-time performance of an FVV system. Furthermore, the individuality of user interaction makes it difficult to serve multiple users simultaneously for a system with conventional architecture. In this paper, we novelly introduce a CNN-based view interpolation algorithm to synthesis dense virtual views in real time. Based on this, we also build an end-to-end live free-viewpoint system with a multi-user oriented streaming strategy. Our system can utilize a single edge server to serve multiple users at the same time without having to bring a large view synthesis load on the client side. We analyze the whole system and show that our approaches give the user a pleasant immersive experience, in terms of both visual quality and latency. © 2022 IEEE.

关键词： Multimedia services

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Research on training image recognition based on deep Convolutional neural networks: A case study of the hand-held insulated ladder recognition model 23

Research on training image recognition based on deep Convolu...

引用

Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Chaobing Wei Jiangmen power supply Bureau Guangdong Power Grid Corporation China

ISBN: (纸本)9798400716430

In this paper, we will introduce a series of work required for the design model of the insulation ladder identification technology, including the construction of the model training environment, the preparation of data sets, the training of the model, the visual operation of the model and the recognition result of the model. The identification model of walking insulation ladder in electric power construction will be developed based on PyTorch. We will ensure the safety of distribution network operations. In this paper, based on the existing YOLOv5 based target detection model, an improved design was carried out to enhance the accuracy of its detection of small targets, and the model was lightweight. Finally, the improved model was deployed to the edge device for hardware experiment verification.

关键词： Deep convolutional neural network image recognition Recognition model Walking insulated ladder

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Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for image Denoising

Deep Convolutional Neural Network Based on Multi-Scale Featu...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Jing Sang, Liu Wan, Zekang Wang, Yuchen Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781728180687

With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.

关键词： image denoising multi-scale convolutional network diamond

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Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265

Neural Frank-Wolfe Policy Optimization for Region-of-Interes...

引用

IEEE visual communications and image processing (VCIP)

作者： Yung-Han Ho Chia-Hao Kao Wen-Hsiao Peng Ping-Chun Hsieh National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9781665475938

This paper presents a reinforcement learning (RL) framework that utilizes Frank-Wolfe policy optimization to solve Coding- Tree-Unit (CTU) bit allocation for Region-of-Interest (ROI) intra-frame coding. Most previous RL-based methods employ the single-critic design, where the rewards for distortion minimization and rate regularization are weighted by an empirically chosen hyper-parameter. Recently, the dual-critic design is proposed to update the actor by alternating the rate and distortion critics. However, its convergence is not guaranteed. To address these issues, we introduce Neural Frank-Wolfe Policy Op-timization (NFWPO) in formulating the CTU-level bit allocation as an action-constrained RL problem. In this new framework, we exploit a rate critic to predict a feasible set of actions. With this feasible set, a distortion critic is invoked to update the actor to maximize the ROI-weighted image quality subject to a rate constraint. Experimental results produced with x265 confirm the superiority of the proposed method to the other baselines.

关键词： image quality image coding visual communication Bit rate Rate distortion theory Reinforcement learning Distortion

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

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Electrical, Computer, communications and Mechatronics Engineering (ICECCME), International conference on

作者： Marjana Tahmid Samiul Alam Kalim Akram Kazi Muhammad Asif Ashrafi Dept. of CSE East Delta University Chittagong Bangladesh Dept. of EEE East Delta University Chittagong Bangladesh Dept. of CSE Ulm University Ulm Germany

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and Computer Vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages" to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions.

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Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Quality comparison of 360° 8K images compressed by conventi...

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International conference on Radioelektronika

作者： Jan Kufa Adam Budac Dept. of Radio Electronics Brno Faculty of Electrical Engineering and Communications Czech Republic

Due to the accessibility of virtual reality in recent years, there has been a great interest in producing and streaming omnidirectional $(360 ^{\circ}$ field of view) high resolution images and videos. Since both high resolution and high quality are demanding for the storage and distribution of such content, the use of advanced compression methods is a key factor in achieving this goal. This paper provides an objective comparison of conventional image compression codecs (JPEG, JPEG XL, HEIC, AVIF, VVC Intra) and deep learning image compression algorithms with a JPEG AI framework recommendation. The visual quality evaluation is based on ten images from publicly available databases compressed to predetermined bit rates. Six full reference objective metrics (WS-PSNR, MS-SSIM, VIFp, FSIMc, GMSD, VMAF) are used to evaluate the visual quality of the compressed images. Modern image compression codecs outperform the oldest and most widely used codec JPEG in terms of bandwidth reduction but require more processing power and system resources.

关键词： Deep learning visualization image coding Codecs image resolution Bit rate Transform coding

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Generalized Gaussian Distribution Based Distortion Model for the H.266/VVC Video Coder

Generalized Gaussian Distribution Based Distortion Model for...

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IEEE visual communications and image processing (VCIP)

作者： Hongkui Wang Junhui Liang Li Yu Yiying Gu Haibing Yin School of Communication Engineering Hangzhou Dianzi University Hangzhou China Lishui Institute of Hangzhou Dianzi University Lishui China College of Electronic Information and Communication-s Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665475938

In versatile video coding (VVC), superior coding performance is achieved with incorporating many advanced coding tools. In this paper, a frame-level coding distortion model is proposed for VVC video coders for the first time. In comparison with the transform coefficient distribution (TCD) of High Effective Video Coding (HEVC), the TCD of VVC has a sharper peak. According to this observation, the TCDs of I, B and P frames are modeled by the probability density function (PDF) of generalized Gaussian distribution (GGD) with three fixed shape parameters. The GGD-based distortion model is then derived with a sliding window-based strategy, i.e., the frame-level coding distortion is formulated as the function of the distribution parameter of frame-level TCD and the quantization step. The experimental results show that the proposed model achieves accurate results of distortion estimation for VVC coders.

关键词： image coding Quantization (signal) Laplace equations Shape visual communication Estimation Transforms

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