检索结果-内蒙古大学图书馆

6th ieee international conference on image processing, applications and Systems, IPAS 2025

ISBN: (纸本)9798331506520

The proceedings contain 86 papers. The topics discussed include: robust real-time monitoring of complex human activities using multi modal video analytics;a robust approach for classifying laparoscopic video distortions using ResNet-50;enhancing x-ray image classification through neural architecture;revolutionary MRI imaging for Alzheimer’s: cutting-edge GANs and vision transformer solutions;advanced deep learning strategies for breast cancer image analysis;identifying surgical instruments in pedagogical cataract surgery videos through an optimized aggregation network;enhancing auxiliary cancer classification task for multi-task breast ultrasound diagnosis network;and bioinspired computer vision for effective extended reality applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of Deep Learning-Based image Recognition Algorithm in signal processing 5

Research on the Application of Deep Learning-Based Image Rec...

引用

5th ieee international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Lv, Jing Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (纸本)9798331533694

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. The generator can produce real images or remove noise by learning the signal image. Then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data. © 2025 ieee.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Optimized image processing on FPGAs: Comparative Analysis of Adder Architectures for image Smoothing applications

Optimized Image Processing on FPGAs: Comparative Analysis of...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Varun, G. Abhinav Chaitanya, R. Jagadeesh, J. Sm, Sakthivel School of Electronics Engineering Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529833

image smoothening is a highly utilized operation in digital image processing focused on reducing the impact of noise. In field programmable gate arrays (FPGA) based implementation, the working of the image smoothening process depends on the arithmetic components, particularly the adder structures. Modifying the underlying adder structures would have a significant impact on the efficiency of image processing. This paper presents a comparative analysis of four advanced adder architectures - like optimized lower part constant or adder (OLOCA), hardware optimized approximate adder with normal error distribution (HOAANED), brent-kung adder, and kogge-stone adder - against the conventional ripple carry adder (RCA). Different adder's impact is evaluated based on delay, power consumption, resource utilization, and peak signal-to-noise ratio (PSNR). Our findings demonstrate how selecting an optimal adder can impact the performance of FPGA-based image-smoothening applications. © 2025 ieee.

关键词： image analysis

来源：评论

学校读者我要写书评

暂无评论

Hypernetwork Design for Self-Supervised Domain Adaptation in Compressive Sensing MRI Reconstruction 6

Hypernetwork Design for Self-Supervised Domain Adaptation in...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Thomas, Libya Zacharias, Joseph Trivandrum Affiliated to: A.P.J Abdul Kalam Technical University College of Engineering Kerala India

ISBN: (纸本)9798331506520

This paper presents a novel hypernetwork design for self-supervised domain adaptation in compressed sensing MRI (CS-MRI) reconstruction. Unlike conventional models, which often fail to generalize across distribution shifts in MRI data, our hypernetwork generates task-specific parameters for a primary reconstruction network based on learned regularization weights, dynamically adjusting to new test data. Leveraging test-time training (TTT), our hypernetwork significantly improves reconstruction accuracy on previously unseen data distributions. Evaluations using the fastMRI dataset demonstrate a notable gain in Peak signal-to-Noise Ratio (PSNR) by 3.6 dB and Structural Similarity Index (SSIM) by 0.05 over baseline models, such as U-Net and VarNet, especially in out-of-distribution settings. © 2025 ieee.

关键词： signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Optimizing Multimodal image Fusion: A Novel Approach with Nystrom Attention Mechanisms in Transformer Models

Optimizing Multimodal Image Fusion: A Novel Approach with Ny...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zeng, Yuqin Wen, Ze Fan, Shuqian Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing China

ISBN: (纸本)9798350368741

This work proposed a new model based on transformers for multimodal image fusion, with explicit attention paid to fusing infrared and visible images toward enhanced detail and information content. This method, which incorporates Nystrom attention and skip connections, optimizes the fusion process so that important features of both modalities are preserved. Traditional models find it hard to keep details in the content and introduce artifacts. On the contrary, this model can efficiently cope with these challenges, as it makes use of an attention mechanism that is computationally highly effective. This will efficiently fuse large-scale image data and may be useful in those fields where very detailed and comprehensive visual information is demanded. The detailed evaluations prove that our model has superior performance over the current state-of-the-art methods in texture detail preservation, thermal differentiation, and overall image quality, which possesses great potential for advanced imaging applications. © 2025 ieee.

关键词： attention mechanism image fusion multimodal imaging transformer

来源：评论

学校读者我要写书评

暂无评论

TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ma, Xinchen Lu, Yue School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloemxxxxc/TRIFORMER. © 2025 ieee.

关键词： image restoration transformer Ultra-high-definition(UHD)

来源：评论

学校读者我要写书评

暂无评论

Improved image Classification with Manifold Neural Networks

Improved Image Classification with Manifold Neural Networks

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Deberaldini Netto, Caio F. Wang, Zhiyang Ruiz, Luana Johns Hopkins University Baltimore United States Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States

ISBN: (纸本)9798350368741

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs' message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs' potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks. © 2025 ieee.

关键词： generalization graph neural networks manifold neural networks variational autoencoders

来源：评论

学校读者我要写书评

暂无评论

Patch based image processing for complex environment characterization 6

Patch based image processing for complex environment charact...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Menier, Corentin Meurie, Cyril Guillemaille, Timothee Ruichek, Yassine Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France UTBM BelfortF-90010 France

ISBN: (纸本)9798331506520

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy. © 2025 ieee.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Dual-Path Consistency Unsupervised Domain Adaptation for Nighttime Semantic Segmentation

Dual-Path Consistency Unsupervised Domain Adaptation for Nig...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Lu, Yuwu Lang, Jicong Ding, Meirong School of Artificial Intelligence South China Normal University Foshan China

ISBN: (纸本)9798350368741

Nighttime semantic segmentation is an indispensable component in practical applications, such as automated vehicles. However, it is often hindered by the lack of annotations due to interference caused by inadequate lighting or exposure. To overcome these difficulties, we propose a Dual-Path Consistency (DPC) unsupervised domain adaptation (UDA) approach. One path is image Darkening Path (IDP), in which feature representations of original images and darkened images extracted from the feature encoder are leveraged to maintain cross-domain style consistency. Another path is image Masking Path (IMP), in which the masked images are reconstructed under the guidance of pseudo-labels, aiming to maintain content consistency in an entirely identical scenario. Extensive experiments on four benchmarks demonstrate the superior performance of the proposed DPC for nighttime semantic segmentation. © 2025 ieee.

关键词： image masking Nighttime semantic segmentation self-training unsupervised domain adaptation

来源：评论

学校读者我要写书评

暂无评论

Several Points are all It Takes: Saluting User-Assisted Single image Reflection Removal

引用

ieee signal processing Letters 2025年 32卷 1735-1739页

作者： He, Lingzhi Chang, Yakun Zhao, Yao Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Visual Intellgence +X International Cooperation Joint Laboratory of MOE Beijing100044 China

Reflection removal is essential for applications in photography, object detection, and augmented reality. Single-image reflection removal (SIRR) offers greater flexibility and applicability than multi-image methods, making it ideal for real-time scenarios. However, strong reflections obscure large portions of the transmission layer, limiting the performance of existing methods. We propose a novel user-assisted approach for SIRR, where users annotate occluded objects by selecting their categories and locations. This provides critical semantic information to guide accurate transmission layer recovery. Additionally, we design a hybrid CNN-Transformer network that leverages local feature extraction and global context modeling to address strong reflection challenges. Experiments on the strong reflection datasets demonstrate the effectiveness of our method, achieving significant improvements in transmission layer recovery and outperforming existing advanced methods across multiple metrics. © 1994-2012 ieee.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：