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基于残差u-net网络的地震资料分辨率提高方法

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中国海洋大学学报（自然科学版） 2025年第1期55卷 140-148页

作者：董博艺张进中国海洋大学海洋地球科学学院山东青岛266100 青岛海洋科技中心海洋矿产资源评价与探测技术功能实验室山东青岛266237 中国海洋大学海底科学与探测技术教育部重点实验室山东青岛266100

高分辨率地震资料处理是获取高品质地震资料、实现薄储层良好地震地质解释的关键。传统提高地震分辨率的方法应用条件苛刻,关键参数求取复杂,在实际应用中受到诸多限制。深度学习中的u-net网络以纯数据驱动的优势,可学习低分辨率地震记... 详细信息

高分辨率地震资料处理是获取高品质地震资料、实现薄储层良好地震地质解释的关键。传统提高地震分辨率的方法应用条件苛刻,关键参数求取复杂,在实际应用中受到诸多限制。深度学习中的u-net网络以纯数据驱动的优势,可学习低分辨率地震记录到高分辨率标签的非线性关系,实现地震资料的高分辨率处理。本文设计了残差u-net网络结构,同时提出了基于概率密度函数控制的同分布反射系数集生成方法,将测井反射系数的概率密度函数作为一种先验约束信息融入训练样本,不仅保证了足够的同分布样本来训练网络,还确保了训练样本更符合工区实际情况,以此提高模型预测的准确性。模型测试和实际资料应用结果表明,本文提出的方法能够有效应用于地震资料分辨率的提高,同时拓宽频带。

关键词：提高分辨率 u-net 残差结构同分布

u-net Inspired Deep Neural network-Based Smoke Plume Detection in Satellite Images

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Computers, Materials & Continua 2024年第4期79卷 779-799页

作者： Ananthakrishnan Balasundaram Ayesha Shaik Japmann Kaur Banga Aman Kumar Singh Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India

Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have beenidentified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions isessential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcingemission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrialsmoke plumes using freely accessible geo-satellite imagery. The existing systemhas so many lagging factors such aslimitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timelyresponse to industrial fires. In this work, the utilization of grayscale images is done instead of traditional colorimages for smoke plume detection. The dataset was trained through a Resnet-50 model for classification and au-net model for segmentation. The dataset consists of images gathered by European Space Agency’s Sentinel-2 satellite constellation from a selection of industrial sites. The acquired images predominantly capture scenesof industrial locations, some of which exhibit active smoke plume emissions. The performance of the abovementionedtechniques and models is represented by their accuracy and IOu (Intersection-over-union) *** images are first trained on the basic RGB images where their respective classification using the Resnet-50model results in an accuracy of 94.4% and segmentation using the u-net Model with an IOu metric of 0.5 andaccuracy of 94% which leads to the detection of exact patches where the smoke plume has occurred. This work hastrained the classification model on grayscale images achieving a good increase in accuracy of 96.4%.

关键词： Smoke plume Resnet-50 u-net geo satellite images early warning global monitoring

Detection of Precipitation Cloud over the Tibet Based on the Improved u-net

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Computers, Materials & Continua 2020年第12期65卷 2455-2474页

作者： Runzhe Tao Yonghong Zhang Lihua Wang Pengyan Cai Haowen Tan School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Chosun UniversityGwangju501759Korea

Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring.u-net,an advanced machine learning(ML)method,is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A(FY-4A).First,in this algorithm,the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model(DEM)has been used as predictor variables for our ***,the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of u-net to residual ***,in order to solve the problem of the network that would produce semantic differences when directly concentrated with low-level and high-level features,we use dense skip pathways to reuse feature maps of different layers as inputs for concatenate neural networks feature layers from different ***,according to the characteristics of precipitation clouds,the pooling layer of u-net was replaced by a convolution operation to realize the detection of small precipitation *** was experimentally concluded that the Pixel Accuracy(PA)and Mean Intersection over union(MIou)of the improved u-net on the test set could reach 0.916 and 0.928,the detection of precipitation clouds over Tibet were well actualized.

关键词： u-net fy-4a precipitation cloud dense skip connections residual network

Inner Cascaded u^(2)-net:An Improvement to Plain Cascaded u-net

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Computer Modeling in Engineering & Sciences 2023年第2期134卷 1323-1335页

作者： Wenbin Wu Guanjun Liu Kaiyi Liang Hui Zhou Tongji university Shanghai201804China Jiading District Central Hospital Affiliated to Shanghai university of Medicine and Health Sciences Shanghai201800China

Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction.u-net has been the baseline model since the very beginning due to a symmetricalu-structure for better feature extraction and fusing and suitable for small *** enhance the segmentation performance of u-net,cascaded u-net proposes to put two u-nets successively to segment targets from coarse to ***,the plain cascaded u-net faces the problem of too less between connections so the contextual information learned by the former u-net cannot be fully used by the latter *** this article,we devise novel Inner Cascaded u-net and Inner Cascaded u^(2)-net as improvements to plain cascaded u-net for medical image *** proposed Inner Cascaded u-net adds inner nested connections between two u-nets to share more contextual *** further boost segmentation performance,we propose Inner Cascaded u^(2)-net,which applies residual u-block to capture more global contextual information from different *** proposed models can be trained from scratch in an end-to-end fashion and have been evaluated on Multimodal Brain Tumor Segmentation Challenge(BraTS)2013 and ISBI Liver Tumor Segmentation Challenge(LiTS)dataset in comparison to related u-net,cascaded u-net,u-net++,u^(2)-net and state-of-the-art *** experiments demonstrate that our proposed Inner Cascaded u-net and Inner Cascaded u^(2)-net achieve better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as get finer outline segmentation.

关键词： Deep neural networks medical image segmentation u-net cascaded convolution block

A Lightweight Model of VGG-u-net for Remote Sensing Image Classification

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Computers, Materials & Continua 2022年第12期73卷 6195-6205页

作者： Mu Ye Li Ji Luo Tianye Li Sihan Zhang Tong Feng Ruilong Hu Tianli Gong He Guo Ying Sun Yu Thobela Louis Tyasi Li Shijun College of Information Technology Jilin Agricultural UniversityChangchun130118China Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center Changchun130118China Jilin Province Intelligent Environmental Engineering Research Center Changchun130118China Jilin Province Information Technology and Intelligent Agriculture Engineering Research Center Changchun130118China College of Engineering and Technology Jilin Agricultural UniversityChangchun130118China Department of Agricultural Economics and Animal Production University of LimpopoSovenga0727PolokwaneSouth Africa College of Information Technology Wuzhou UniversityWuzhou543003China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543003China

Remote sensing image analysis is a basic and practical research hotspot in remote sensing *** sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other *** this paper,we propose a lightweight model combining vgg-16 and u-net *** combining two convolutional neural networks,we classify scenes of remote sensing *** ensuring the accuracy of the model,try to reduce the memory of *** to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 *** the same time,The classification and convergence speed of the model are greatly *** simultaneously take the remote sensing scene image of 64×64 as input into the designed *** the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low ***,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.

关键词： VGG-16 u-net fewer feature points nonlinear correction layer zero padding

Application of u-net and Optimized Clustering in Medical Image Segmentation:A Review

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2173-2219页

作者： Jiaqi Shao Shuwen Chen Jin Zhou Huisheng Zhu Ziyi Wang Mackenzie Brown School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China School of Data Science Perdana UniversitySerdangSelangor43400Malaysia

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so *** with natural images,medical images have a variety of ***,the emphasis of information which is conveyed by images of different modes is quite *** it is time-consuming and inefficient to manually segment medical images only by professional and experienced ***,large quantities of automated medical image segmentation methods have been ***,until now,researchers have not developed a universal method for all types of medical image *** paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent *** the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation *** is the improved strategies based on traditional clustering *** other is the research progress of the improved image segmentation network structure model based on *** power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional *** paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

关键词： Medical image segmentation clustering algorithm u-net

A u-net-Based CNN Model for Detection and Segmentation of Brain Tumor

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Computers, Materials & Continua 2023年第1期74卷 1333-1349页

作者： Rehana Ghulam Sammar Fatima Tariq Ali Nazir Ahmad Zafar Abdullah A.Asiri Hassan A.Alshamrani Samar M.Alqhtani Khlood M.Mehdar Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Radiological Sciences Department College of applied medical sciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia

Human brain consists of millions of cells to control the overall structure of the human *** these cells start behaving abnormally,then brain tumors *** and initial stage brain tumor detection has always been an issue in the field of medicines for medical *** handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human *** this purpose,we proposed a novel u-net-based Convolutional Neural network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 *** initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation *** model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques.

关键词： u-net brain tumor magnetic resonance images convolutional neural network segmentation

ICA-unet:An improved u-net network for brown adipose tissue segmentation

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Journal of Innovative Optical Health Sciences 2022年第3期15卷 70-80页

作者： Haolin Wang Zhonghao Wang Jingle Wang Kang Li Guohua Geng Fei Kang Xin Cao School of Information Sciences and Technology Northwest university Xi'anShaanxi 710127P.R.China Department of Nuclear Medicine Xijing Hospital Fourth Military Medical University Xi'anShaanxi 710127P.R.China university of Wisconsin-Madison Madison Wisconsin 53715USA

Brown adipose tissue(BAT)is a kind of adipose tissue engaging in thermoregulatory thermogenesis,metaboloregulatory thermogenesis,and *** studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related ***,the activity of BAT presents certain differences between different ages and ***,BAT segmentation based on PET/CT data is a reliable method for brown fat ***,most of the current BAT segmentation methods rely on the experience of *** this paper,an improved u-net network,ICA-unet,is proposed to achieve automatic and precise segmentation of ***,the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional(Do-Conv)***,the channel attention block is introduced between the double-layer ***,the image information entropy(IIE)block is added in the skip connections to strengthen the edge ***,the performance of this method is evaluated on the dataset of PET/CT images from 368 *** results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by *** average DICE coeffcient(DSC)is 0.9057,and the average Hausdorff distance is *** results suggest that the method proposed in this paper can achieve effcient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.

关键词： PET/CT segmentation of brown adipose tissue u-net medical image processing deep learning

Ore Image Segmentation Method Based on u-net and Watershed

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Computers, Materials & Continua 2020年第10期65卷 563-578页

作者： Hui Li Chengwei Pan Ziyi Chen Aziguli Wulamu Alan Yang School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Amphenol AssembleTech Houston77070USA

Ore image segmentation is a key step in an ore grain size analysis based on image *** traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and *** this article,in order to solve the problem,an ore image segmentation method based on u-net is *** adjust the structure of u-net to speed up the processing,and we modify the loss function to enhance the generalization of the *** the collection of the ore image,we design the annotation standard and train the network with the annotated ***,the marked watershed algorithm is used to segment the adhesion *** experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high *** has great practical value to the actual ore grain statistical task.

关键词： Image segmentation ore grain size u-net watershed algorithm