检索结果-内蒙古大学图书馆

A Statistical Framework to Investigate the Optimality of signal-Reconstruction methods

IEEE TRANSACTIONS ON signal processing 2023年 71卷 2043-2055页

作者： Bohra, Pakshal Pla, Pol del Aguila Giovannelli, Jean-Francois Unser, Michael Ecole Polytech Fed Lausanne Biomed Imaging Grp CH-1015 Lausanne Switzerland CIBM Ctr Biomed Imaging CH-1015 Lausanne Switzerland Univ Bordeaux IMS CNRS B INPUMR 5218 F-33400 Talence France

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizations of sparse stochastic processes, which makes them ideally matched to variational sparsity-promoting techniques. We derive Gibbs sampling schemes to compute the minimum mean-square error estimators for processes with Laplace, Student's t, and Bernoulli-Laplace innovations. These allow our framework to provide quantitative measures of the degree of optimality (in the mean-square-error sense) for any given reconstruction method. We showcase our framework by benchmarking the performance of some well-known variational methods and convolutional neural network architectures that perform direct nonlinear reconstructions in the context of deconvolution and Fourier sampling. Our experimental results support the understanding that, while these neural networks outperform the variational methods and achieve near-optimal results in many settings, their performance deteriorates severely for signals associated with heavy-tailed distributions.

关键词： Inverse problems minimum mean-square error convolutional neural networks sparse stochastic processes

来源：评论

学校读者我要写书评

暂无评论

Graph Linear Canonical Transform: Definition, Vertex-Frequency Analysis and Filter Design

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 5691-5707页

作者： Chen, Jian Yi Zhang, Yu Li, Bing Zhao Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab MCAACI Beijing 100081 Peoples R China

This paper proposes a graph linear canonical transform (GLCT) by decomposing the linear canonical parameter matrix into fractional Fourier transform, scale transform, and chirp modulation for graph signal processing. The GLCT enables adjustable smoothing modes, enhancing alignment with graph signals. Leveraging traditional fractional domain time-frequency analysis, we investigate vertex-frequency analysis in the graph linear canonical domain, aiming to overcome limitations in capturing local information. Filter design methods, including optimal design and learning with stochastic gradient descent, are analyzed and applied to image classification tasks. The proposed GLCT and vertex-frequency analysis present innovative approaches to signal processing challenges, with potential applications in various fields.

关键词： Filtering theory Transforms signal processing Matrix decomposition Laplace equations Band-pass filters Atoms Symmetric matrices Fourier transforms Eigenvalues and eigenfunctions Graph signal processing graph linear canonical transform vertex-frequency analysis filter design

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Convolutional and Graph neural Network for Human Action Detection in Static images

引用

CIRCUITS SYSTEMS AND signal processing 2024年第12期43卷 7820-7842页

作者： Lu, Xinbiao Xing, Hao Hohai Univ Coll Artificial Intelligence & Automat Nanjing Jiangsu Peoples R China

Human action detection in static images is a hot and challenging field within computer vision. Given the limited features of a single image, achieving precision detection results require the full utilization of the image's intrinsic features, as well as the integration of methods from other fields to process the images for generating additional features. In this paper, we propose a novel dual pathway model for action detection, whose main pathway employs a convolutional neural network to extract image features and predict the probability of the image belonging to each respective action. Meanwhile, the auxiliary pathway uses a pose estimate algorithm to obtain human key points and connection information for constructing a graphical human model for each image. These graphical models are then transformed into graph data and input into a graph neural network for features extracting and probability prediction. Finally, a corresponding connected neural network propose by us is used to fusing the probability vectors generated from the two pathways, which learns the weight of each action class in each vector to enable their subsequent fusion. It is noted that transfer learning is also used in our model to improve the training speed and detection accuracy of it. Experimental results upon three challenging datasets: Stanford40, PPMI and MPII illustrate the superiority of the proposed method.

关键词： Human action detection Static images Graphical human model Corresponding connected neural network Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A three stage framework for abnormality detection in sperm cell images using CNN

引用

BIOMEDICAL signal processing AND CONTROL 2025年 99卷

作者： Prabaharan, L. Saravanan, N. SASTRA Deemed Be Univ Sch Comp Thanjavur Tamilnadu India

image analysis is crucial for microscopic medical images, particularly for imaging sperm cells. Sperm morphology analysis, a crucial process of assisted fertilization techniques, can be used to evaluate male infertility, which significantly impacts couples' quality of life. This paper proposes a technique that combines convolutional neural networks (CNN) with modified Havrda-Charvat entropic segmentation to identify normal sperm cells in pre-processed image samples. Initially, a noise removal algorithm is applied to the sperm cell images, followed by segmentation using the modified Havrda-Charvat entropy method to isolate individual sperm cells. High detection accuracy is then achieved through a combination of deep learning and feature extraction. This research optimizes three stages: image pre-processing with a Wiener filter, segmentation using the Havrda-Charvat entropy technique, and abnormality detection with CNN. The proposed method achieves 98.99% accuracy in identifying normal sperm cells based on their morphology, outperforming state-of-the-art techniques. By enhancing sperm cell analysis methods, this research facilitates more precise and automated segmentation, processing, and detection. The proposed approach has the potential to revolutionize reproductive medicine by improving the accuracy of fertility diagnoses and the effectiveness of treatments.

关键词： Spermatozoa image processing Deep learning Segmentation Morphology Evaluation metrics

来源：评论

学校读者我要写书评

暂无评论

image denoising using adaptive bi-dimensional stochastic resonance system

引用

FERROELECTRICS 2023年第1期609卷 148-157页

作者： Wang, Shan Niu, Pingjuan Li, Yong Jia, Jiangkai Wang, Shuai Li, Huichao Sun, Bo Zheng, Bin Ximin, Sun Tianjin Univ Technol Sch Mech Engn Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin Peoples R China State Grid Ecommerce Technol Co Ltd Tianjin Peoples R China

Using stochastic resonance (SR) mechanism, the output signal can be enhanced by adding noise to the nonlinear system. Therefore, an image denoising algorithm based on adaptive bi-dimensional stochastic resonance (ABSR) is proposed in this paper. Firstly, the image is sampled as a bi-dimensional signal, and an adaptive bi-dimensional dynamic nonlinear system model is constructed. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the output image are used as the double evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system using the reverse positioning method. Compared with the traditional mean filter, median filter and one-dimensional stochastic resonance, the image restoration effect of dynamic adaptive bi-dimensional stochastic resonance is more closer to the original image, and the histogram, PSNR and SSIM of the output image are also significantly better than the other three methods. The results show that dynamic adaptive bi-dimensional stochastic resonance has better denoising effect and better robustness to the change of noise intensity in image processing.

关键词： image denoising stochastic resonance bi-dimensional system

来源：评论

学校读者我要写书评

暂无评论

Salt-and-pepper denoising based on lightweight convolutional neural networks for flexible AMOLED

引用

IET image processing 2025年第1期19卷

作者： Huang, Chengqiang Yang, Yanjun He, Yinghu Zunyi Normal Univ Sch Phys & Elect Sci Zunyi 563006 Peoples R China

To improve the performance of the image preprocessing module in consumer electronics using an active-matrix organic light-emitting diode display panel, the concept of judging before processing for salt-and-pepper denoising is originally proposed. Firstly, a dataset for salt-and-pepper noise image classification is constructed, and a convolutional neural network (CNN) for judging noise image (CNN-J) is trained. image classified as normal by CNN-J is not processed, while the classified noisy image is denoised. In the denoising process, a marking image and a rough denoised image are generated by CNN for noise mask (CNN-M) and CNN for denoising (CNN-D), respectively. Subsequently, the refined denoised image is output using the proposed refining mechanism. The middle layers of CNN-M and CNN-D are constructed by depth-separable CNN to reduce the network complexity. Experimental results show that the misjudging rate of CNN-M marking is reduced by 19.94% compared with the best existing marking method. Compared with the traditional methods, the peak signal to noise ratio of the proposed method is increased by 2.95% and the information loss is reduced by 21.46%. In addition, the computational complexity is at least 11.18% lower than that of the traditional CNN. Finally, the display of salt-and-pepper denoised images on the flexible AMOLED is realized.

关键词： image denoising image enhancement

来源：评论

学校读者我要写书评

暂无评论

Spiking two-stream methods with unsupervised STDP-based learning for action recognition

引用

signal processing-image COMMUNICATION 2025年 134卷

作者： El-Assal, Mireille Tirilly, Pierre Bilasco, Ioan Marius Univ Lille Cent Lille CRIStAL Ctr Rech Informat Signal & Automat Lille CNRSMR 9189 F-59000 Lille France

Video analysis is a computer vision task that is useful for many applications like surveillance, human-machine interaction, and autonomous vehicles. Deep learning methods are currently the state-of-the-art methods for video analysis. Particularly, two-stream methods, which leverage both spatial and temporal information, have proven to be valuable in Human Action Recognition (HAR). However, they have high computational costs, and need a large amount of labeled data for training. In addressing these challenges, this paper adopts amore efficient approach by leveraging Convolutional Spiking neural Networks (CSNNs) trained with the unsupervised Spike Timing-Dependent Plasticity (STDP) learning rule for action classification. These networks represent the information using asynchronous low-energy spikes, which allows the network to be more energy efficient when implemented on neuromorphic hardware. Furthermore, learning visual features with unsupervised learning reduces the need for labeled data during training, making the approach doubly advantageous. Therefore, we explore transposing two-stream convolutional neural networks into the spiking domain, where we train each stream with the unsupervised STDP learning rule. We investigate the performance of these networks in video analysis by employing five distinct configurations for the temporal stream, and evaluate them across four benchmark HAR datasets. In this work, we show that two-stream CSNNs can successfully extract spatio-temporal information from videos despite using limited training data, and that the spiking spatial and temporal streams are complementary. We also show that replacing a dedicated temporal stream with a spatio-temporal one within a spiking two-stream architecture leads to information redundancy that hinders the performance.

关键词： Spiking neural networks STDP Action classification Two-stream architecture Spatial and temporal features 3D convolution

来源：评论

学校读者我要写书评

暂无评论

How transfer learning is used in generative models for image classification: improved accuracy

引用

signal image AND VIDEO processing 2025年第1期19卷 1-9页

作者： Ebrahimzadeh, Danial Sharif, Sarah Banad, Yaser Univ Oklahoma Sch Elect & Comp Engn Norman OK 73019 USA

Recent breakthroughs in generative neural networks have paved the way for transformative capabilities, particularly in their capacity to generate novel data, notably in the realm of images. The integration of these models with the increasingly popular technique of transfer learning, designed for proficient feature extraction, holds the promise of enhancing overall performance. This paper delves into the exploration of employing generative models in conjunction with transfer learning methods for feature extraction, with a specific focus on image classification tasks. Our investigation aims to scrutinize the effectiveness of leveraging generative models alongside pre-trained models as feature extractors in the context of image classification. To the best of our knowledge, our investigation is the first to link transfer learning and generative models for a discriminative task under one roof. The proposed approach undergoes rigorous evaluation on two distinct datasets, employing specific metrics to gauge the model's performance. The results exhibit a notable nearly 10% enhancement achieved through the integration of generative models, underscoring their potential for achieving heightened accuracy in image classification. These findings highlight significant advancements in image classification accuracy, surpassing the performance of conventional Artificial neural Network (ANN) models.

关键词： Generative adversarial networks Generative models Restricted Boltzmann machines Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Transformer Convolutional Network With the Method of image Segmentation for EEG-Based Emotion Recognition

引用

IEEE signal processing LETTERS 2024年 31卷 401-405页

作者： Zhang, Xinyiy Cheng, Xiankai Univ Sci & Technol China Sch Biomed Engn Suzhou Div Life Sci & Med Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou 215163 Peoples R China

Electroencephalogram (EEG) based emotion recognition has become an important topic in human-computer interaction and affective computing. However, existing advanced methods still have some problems. Firstly, using too many electrodes will decrease the practicality of EEG acquisition device. Secondly, transformer is not good at extracting local features. Finally, differential entropy (DE) is unsuitable for extracting features outside the 2-44 Hz frequency band. To solve these problems, we designed a neural network using 14 electrodes, utilizing differential entropy and designed spectrum sum (SS) to extract features, using convolutional neural networks and image segmentation techniques to learn local features, and transformer encoders to learn global features. The model outperformed advanced methods with classification results of 98.50% and 99.00% on the SEED-IV and SEED-V datasets.

关键词： Feature extraction Electroencephalography Transformers image segmentation Emotion recognition Convolutional neural networks Tensors Electroencephalogram (EEG) emotion recognition transformer image segmentation

来源：评论

学校读者我要写书评

暂无评论

Complex-Valued Autoencoder-Based neural Data Compression for SAR Raw Data

引用

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第3期19卷 572-582页

作者： Asiyabi, Reza Mohammadi Datcu, Mihai Anghel, Andrei Focsa, Adrian Martone, Michele Rizzoli, Paola Imbembo, Ernesto Natl Univ Sci & Technol POLITEHN Bucharest Res Ctr Spatial Informat CEOSpaceTech Bucharest 060042 Romania Mil Tech Acad Ferdinand I Bucharest 050141 Romania Microwaves & Radar Inst German Aerosp Ctr DLR D-51147 Wessling Germany ESA ESTEC European Space Agcy NL-2200 AG Noordwijk Netherlands

Recent advances in Synthetic Aperture Radar (SAR) sensors and innovative advanced imagery techniques have enabled SAR systems to acquire very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. The improvements of the SAR system capabilities also imply a significant increase in SAR data acquisition rates, such that efficient and effective compression methods become necessary. The compression of SAR raw data plays a crucial role in addressing the challenges posed by downlink and memory limitations onboard the SAR satellites and directly affects the quality of the generated SAR image. neural data compression techniques using deep models have attracted many interests for natural image compression tasks and demonstrated promising results. In this study, neural data compression is extended into the complex domain to develop a Complex-Valued (CV) autoencoder-based data compression for SAR raw data. To this end, the basic fundamentals of data compression and Rate-Distortion (RD) theory are reviewed, well known data compression methods, Block Adaptive Quantization (BAQ) and JPEG2000 methods, are implemented and tested for SAR raw data compression, and a neural data compression based on CV autoencoders is developed for SAR raw data. Furthermore, since the available Sentinel-1 SAR raw products are already compressed with Flexible Dynamic BAQ (FDBAQ), an adaptation procedure applied to the decoded SAR raw data to generate SAR raw data with quasi-uniform quantization that resemble the statistics of the uncompressed SAR raw data onboard the satellites.

关键词： Synthetic aperture radar Quantization (signal) Transform coding image coding Sentinel-1 Standards Satellites Radar polarimetry Correlation Rate-distortion Data compression neural data compression rate-distortion theory SAR raw data compression

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：