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Computational Imaging IV最新文献

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Front Matter: Volume 10990 封面:卷10990
Computational Imaging IV Pub Date : 2019-07-22 DOI: 10.1117/12.2536354
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引用次数: 0
Deep neural networks for sparse-view filtered backprojection imaging 稀疏视图滤波反向投影成像的深度神经网络
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2513681
Cain Gantt, Yuanwei Jin, E. Lu
{"title":"Deep neural networks for sparse-view filtered backprojection imaging","authors":"Cain Gantt, Yuanwei Jin, E. Lu","doi":"10.1117/12.2513681","DOIUrl":"https://doi.org/10.1117/12.2513681","url":null,"abstract":"Though effective and computationally efficient algorithms have been developed, the commonly utilized filtered backprojection (FBP) approach to computed tomography (CT) reconstruction suffers from artifact production in sparse-view applications. Within the past few years, convolutional neural networks (CNNs) have been applied to enhance sparse-view reconstruction in CT imaging. Using a network trained on sparse-view FBP reconstructions, the artifacts introduced by undersampling the imaging space can be removed. In this paper, we investigate specific choices in the implementation of the CNN, including the network architecture, training parameters, and data preprocessing, to determine effects on the images produced by the network. Our proposed algorithm and implementation strategies improve upon the use of FBP algorithms alone by removing artifacts produced during sparse-view CT reconstruction.","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Theoretical analysis of diffraction imaging in Fourier ptychography microscopy 傅里叶平面摄影显微镜衍射成像的理论分析
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2519587
Shaohui Zhang, Yao Hu, Ying Wang, Xuemin Cheng, Q. Hao
{"title":"Theoretical analysis of diffraction imaging in Fourier ptychography microscopy","authors":"Shaohui Zhang, Yao Hu, Ying Wang, Xuemin Cheng, Q. Hao","doi":"10.1117/12.2519587","DOIUrl":"https://doi.org/10.1117/12.2519587","url":null,"abstract":"","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep learning in computational microscopy 计算机显微镜中的深度学习
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2520089
Thanh Nguyen, G. Nehmetallah, L. Tian
{"title":"Deep learning in computational microscopy","authors":"Thanh Nguyen, G. Nehmetallah, L. Tian","doi":"10.1117/12.2520089","DOIUrl":"https://doi.org/10.1117/12.2520089","url":null,"abstract":"We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Fully-flexible glass-air disordered fiber imaging through deep learning (Conference Presentation) 基于深度学习的全柔性玻璃-空气无序纤维成像(会议报告)
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2521247
S. Pang, Yangyang Sun, Jian Zhao, A. Schülzgen
{"title":"Fully-flexible glass-air disordered fiber imaging through deep learning (Conference Presentation)","authors":"S. Pang, Yangyang Sun, Jian Zhao, A. Schülzgen","doi":"10.1117/12.2521247","DOIUrl":"https://doi.org/10.1117/12.2521247","url":null,"abstract":"","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134224305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Propagation-based and mesh-based x-ray quantitative phase imaging with conventional sources 基于传播和网格的传统光源x射线定量相位成像
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2520756
Weiyuan Sun, C. MacDonald, J. Petruccelli
{"title":"Propagation-based and mesh-based x-ray quantitative phase imaging with conventional sources","authors":"Weiyuan Sun, C. MacDonald, J. Petruccelli","doi":"10.1117/12.2520756","DOIUrl":"https://doi.org/10.1117/12.2520756","url":null,"abstract":"The contrast in conventional x-ray imaging is generated by differential attenuation of x rays, which is generally very small in soft tissue. Phase imaging has been shown to improve contrast and signal to noise ratio (SNR) by factors of 100 or more. However, acquiring phase images typically requires a highly spatially coherent source (e.g. a 50 μm or smaller microfocus source or a synchrotron facility), or multiple images acquired with precisely aligned gratings. Here we demonstrate two phase imaging techniques compatible with conventional sources: polycapillary focusing optics to enhance source coherence and mesh-based structured illumination.","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123008876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
PlumeNET: a convolutional neural network for plume classification in thermal imagery 用于热图像中羽流分类的卷积神经网络
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2518763
Christian W. Smith, J. R. Dupuis, W. Marinelli
{"title":"PlumeNET: a convolutional neural network for plume classification in thermal imagery","authors":"Christian W. Smith, J. R. Dupuis, W. Marinelli","doi":"10.1117/12.2518763","DOIUrl":"https://doi.org/10.1117/12.2518763","url":null,"abstract":"","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A comparative investigation on the use of compressive sensing methods in computational ghost imaging 压缩感知方法在计算鬼影成像中的应用比较研究
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2518594
Chen Zhang, Bincheng Zhu, S. Semper, A. Breitbarth, M. Rosenberger, G. Notni
{"title":"A comparative investigation on the use of compressive sensing methods in computational ghost imaging","authors":"Chen Zhang, Bincheng Zhu, S. Semper, A. Breitbarth, M. Rosenberger, G. Notni","doi":"10.1117/12.2518594","DOIUrl":"https://doi.org/10.1117/12.2518594","url":null,"abstract":"Usually, a large number of patterns are needed in the computational ghost imaging (CGI). In this work, the possibilities to reduce the pattern number by integrating compressive sensing (CS) algorithms into the CGI process are systematically investigated. Based on the different combinations of sampling patterns and image priors for the L1-norm regularization, different CS-based CGI approaches are proposed and implemented with the iterative shrinkage thresholding algorithm. These CS-CGI approaches are evaluated with various test scenes. According to the quality of the reconstructed images and the robustness to measurement noise, a comparison between these approaches is drawn for different sampling ratios, noise levels, and image sizes.","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116058362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Microscopic image enhancement based on Fourier ptychography technique 基于傅里叶平面摄影技术的显微图像增强
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2519602
Ying Wang, Guocheng Zhou, Yao Hu, Xuemin Cheng, Hao Qun, Shaohui Zhang
{"title":"Microscopic image enhancement based on Fourier ptychography technique","authors":"Ying Wang, Guocheng Zhou, Yao Hu, Xuemin Cheng, Hao Qun, Shaohui Zhang","doi":"10.1117/12.2519602","DOIUrl":"https://doi.org/10.1117/12.2519602","url":null,"abstract":"Image enhancement technique is utilized to emphasize the overall or local characteristics of pictures and widely used in aerospace, and machine vision application. However, most of these techniques are mathematical algorithms based on captured pictures instead of the imaging process. Fourier ptychographic microscopy (FPM) is a recently developed computational imaging approach which stitches together low-resolution images acquired under different angles of illumination with the same intensity in Fourier space to produce a wide-field, high-resolution complex sample image. In this article, a theoretical model about the illumination intensity is proposed. The effect of uneven illumination intensity can be reduced significantly based on our model. Furthermore, the quality of the reconstructed image can be enhanced by adjusting the intensity of the illumination light corresponding to the high frequency components of the original spectrum.","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131069473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Quantitative phase imaging camera with a weak diffuser based on the transport of intensity equation 基于强度输运方程的弱扩散器定量相位成像相机
Computational Imaging IV Pub Date : 2019-05-13 DOI: 10.1117/12.2521367
Linpeng Lu, Jiasong Sun, Jialin Zhang, Yao Fan, Qian Chen, C. Zuo
{"title":"Quantitative phase imaging camera with a weak diffuser based on the transport of intensity equation","authors":"Linpeng Lu, Jiasong Sun, Jialin Zhang, Yao Fan, Qian Chen, C. Zuo","doi":"10.1117/12.2521367","DOIUrl":"https://doi.org/10.1117/12.2521367","url":null,"abstract":"","PeriodicalId":127697,"journal":{"name":"Computational Imaging IV","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132048707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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