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Spatial and Contextual Path Network for Image Inpainting

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Intelligent Automation & Soft Computing 2024年第2期39卷 115-133页

作者： dengyong zhang Yuting Zhao Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinTaiwan Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Image inpainting is a kind of use known area of information technology to repair the loss or damage to the *** feature extraction is the core of image *** enough space for information and a larger receptive field is very important to realize high-precision image ***,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same *** order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path *** the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial *** the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept *** order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two *** fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight *** of images in order to extract context information,we add attention to the context path refinement *** modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective *** show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements.

关键词： Image inpainting attention deep learning convolutional network

Long non-coding RNA colon cancer-associated transcript 2:role and function in human cancers

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Chinese Medical Journal 2022年第23期135卷 2785-2797页

作者： Shuoshuo Ma Wenjing Wang dengyong zhang Guanru Zhao Zheng Lu Department of General Surgery The First Affiliated Hospital of Bengbu Medical CollegeBengbuAnhui 233000China The Second Affiliated Hospital of Anhui Medical University HefeiAnhui 230032China

Long non-coding RNAs(lncRNAs)are a family of non-protein-coding RNAs that span a length of over 200 *** reports have illustrated that lncRNAs are involved in various cellular processes and that their abnormal expression leads to the occurrence and development of various *** cancer-associated transcript 2(CCAT2)was first reported as an oncogene in colon *** CCAT2 is abnormally expressed in hepatocellular carcinoma,cholangiocarcinoma,lung cancer,breast cancer,ovarian cancer,glioma,and other *** tumor tissues,abnormally overexpressed CCAT2 can affect cell proliferation,migration,epithelial-mesenchymal transition,apoptosis,and other biological behaviors through endogenous RNAs mechanisms,various signaling pathways,transcriptional regulation,and other complex ***,the overexpression of CCAT2 is also closely related to the tumor size,tumor node metastasis(TNM)stage,survival time,and other prognostic factors,suggesting that it is a potential prognostic *** article reviews the biological functions of CCAT2 and its mechanisms of action in tumors from previous *** this review,we attempt to provide a molecular basis for future clinical applications of lncRNA CCAT2.

关键词： Cancer CCAT2 Long non-coding RNA Colon cancer-associated transcript 2

DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

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Computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao dengyong zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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Computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： dengyong zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

Image Inpainting Detection Based on High-Pass Filter Attention Network

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Computer Systems Science & Engineering 2022年第12期43卷 1145-1154页

作者： Can Xiao Feng Li dengyong zhang Pu Huang Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409USA

Image inpainting based on deep learning has been greatly *** original purpose of image inpainting was to repair some broken photos, suchas inpainting artifacts. However, it may also be used for malicious operations,such as destroying evidence. Therefore, detection and localization of imageinpainting operations are essential. Recent research shows that high-pass filteringfull convolutional network (HPFCN) is applied to image inpainting detection andachieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, weintroduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtainmore information. Channel attention (cSE) is introduced in upsampling toenhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.

关键词： Image inpainting detection spatial attention channel attention full convolutional network high-pass filter

Small Object Detection via Precise Region-Based Fully Convolutional Networks

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Computers, Materials & Continua 2021年第11期69卷 1503-1517页

作者： dengyong zhang Jiawei Hu Feng Li Xiangling Ding Arun Kumar Sangaiah Victor SSheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

In the past several years,remarkable achievements have been made in the field of object *** performance is generally improving,the accuracy of small object detection remains low compared with that of large object *** addition,localization misalignment issues are common for small objects,as seen in GoogLeNets and residual networks(ResNets).To address this problem,we propose an improved region-based fully convolutional network(R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest(PS-RoI)pooling with position-sensitive precise region of interest(PS-Pr-RoI)pooling,which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps,thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context(MS COCO)training dataset was *** showed an accuracy improvement of 3.7%for object detection tasks and an increase of 6.0%for small objects.

关键词： Small object detection precise R-FCN PS-Pr-RoI pooling two-stage detector

Empirical Comparisons of Deep Learning Networks on Liver Segmentation

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Computers, Materials & Continua 2020年第3期62卷 1233-1247页

作者： Yi Shen Victor S.Sheng Lei Wang Jie Duan Xuefeng Xi dengyong zhang Ziming Cui School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhouChina Department of Computer Science University of Central ArkansasConwayArkansasUSA Huan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver *** recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver *** them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many *** have achieved certain achievements in medical image *** this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver *** original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live *** experimental results show that Densenet performs the best on liver segmentation,followed by *** perform much better than Segnet,U-Net,and *** Segnet,U-Net,and FCN,U-Net performs the best,followed by *** performs the worst.

关键词： Liver segmentation deep learning FCN U-Net Segnet Resnet Densenet

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基于数字孪生的机械加工车间多级能效监测

计算机集成制造系统 2023年第6期29卷 2102-2117页

作者：李聪波曹宝吴畏张登永赵继烜重庆大学机械传动国家重点实验室重庆400044

针对机械加工车间能耗规律复杂、设备状态时变性强导致能效实时全面监测困难的问题,提出一种基于数字孪生的机械加工车间多级能效监测方法。通过分析机械加工车间的多源能耗特性,建立机械加工车间多级能效监测指标体系;提出机械加工车... 详细信息

针对机械加工车间能耗规律复杂、设备状态时变性强导致能效实时全面监测困难的问题,提出一种基于数字孪生的机械加工车间多级能效监测方法。通过分析机械加工车间的多源能耗特性,建立机械加工车间多级能效监测指标体系;提出机械加工车间多源异构数据的获取方法,并构建事件与实时数据驱动的机械加工车间实时映射模型;基于碰撞检测与布尔运算的物理模型动态判别机床运行状态,进而获得基于数字孪生的机械加工车间多级能效监测方法。以某机床企业机械加工车间为应用案例,验证了所提方法的有效性和实用性。

关键词：能效监测数字孪生机械加工车间多级能效

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基于双分支网络的图像修复取证方法

网络与信息安全学报 2022年第6期8卷 110-122页

作者：章登勇文凰李峰曹鹏向凌云杨高波丁湘陵长沙理工大学湖南省综合交通运输大数据智能处理重点实验室湖南长沙410114 长沙理工大学计算机与通信工程学院湖南长沙410114 湖南大学信息科学与工程学院湖南长沙410082 湖南科技大学计算机科学与工程学院湖南湘潭411004

图像修复是一项利用图像已知区域的信息来修复图像中缺失或损坏区域的技术。人们借助以此为基础的图像编辑软件无须任何专业基础就可以轻松地编辑和修改数字图像内容,一旦图像修复技术被用于恶意移除图像的内容,会给真实的图像带来信任... 详细信息

图像修复是一项利用图像已知区域的信息来修复图像中缺失或损坏区域的技术。人们借助以此为基础的图像编辑软件无须任何专业基础就可以轻松地编辑和修改数字图像内容,一旦图像修复技术被用于恶意移除图像的内容,会给真实的图像带来信任危机。目前图像修复取证的研究只能有效地检测某一种类型的图像修复。针对这一问题,提出了一种基于双分支网络的图像修复被动取证方法。双分支中的高通滤波卷积网络先使用一组高通滤波器来削弱图像中的低频分量,然后使用4个残差块提取特征,再进行两次4倍上采样的转置卷积对特征图进行放大,此后使用一个5×5的卷积来减弱转置卷积带来的棋盘伪影,生成图像高频分量上的鉴别特征图。双分支中的双注意力特征融合分支先使用预处理模块为图像增添局部二值模式特征图。然后使用双注意力卷积块自适应地集成图像局部特征和全局依赖,捕获图像修复区域和原始区域在内容及纹理上的差异,再对双注意力卷积块提取的特征进行融合。最后对特征图进行相同的上采样,生成图像内容和纹理上的鉴别特征图。实验结果表明该方法在检测移除对象的修复区域上,针对样本块修复方法上检测的F1分数较排名第二的方法提高了2.05%,交并比上提高了3.53%;针对深度学习修复方法上检测的F1分数较排名第二的方法提高了1.06%,交并比提高了1.22%。对结果进行可视化可以看出,在检测修复区域上能够准确地定位移除对象的边缘。

关键词：图像取证图像修复检测深度学习注意力机制