TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion
出版年份 2020 全文链接
标题
TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion
作者
关键词
-
出版物
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
Volume 37, Issue 3, Pages 422
出版商
The Optical Society
发表日期
2020-01-09
DOI
10.1364/josaa.375595
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
- (2018) Andreas Hauptmann et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
- (2018) Eunhee Kang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
- (2018) Qingsong Yang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network
- (2018) Hongming Shan et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Denoising imaging polarimetry by adapted BM3D method
- (2018) Alexander B. Tibbs et al. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
- TomoBank: a tomographic data repository for computational x-ray science
- (2018) Francesco De Carlo et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Low-dose x-ray tomography through a deep convolutional neural network
- (2018) Xiaogang Yang et al. Scientific Reports
- X-ray tomography of extended objects: a comparison of data acquisition approaches
- (2018) Ming Du et al. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- (2017) Kyong Hwan Jin et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Generative Adversarial Networks for Noise Reduction in Low-Dose CT
- (2017) Jelmer M. Wolterink et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- XDesign: an open-source software package for designing X-ray imaging phantoms and experiments
- (2017) Daniel J. Ching et al. JOURNAL OF SYNCHROTRON RADIATION
- A Perspective on Deep Imaging
- (2016) Ge Wang IEEE Access
- Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data
- (2016) Daniël M. Pelt et al. JOURNAL OF SYNCHROTRON RADIATION
- Hyperspectral image denoising using the robust low-rank tensor recovery
- (2015) Chang Li et al. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
- Deep learning
- (2015) Yann LeCun et al. NATURE
- State of the Art: Iterative CT Reconstruction Techniques
- (2015) Lucas L. Geyer et al. RADIOLOGY
- The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography
- (2015) Wim van Aarle et al. ULTRAMICROSCOPY
- Super-resolution: a comprehensive survey
- (2014) Kamal Nasrollahi et al. MACHINE VISION AND APPLICATIONS
- scikit-image: image processing in Python
- (2014) Stéfan van der Walt et al. PeerJ
- TomoPy: a framework for the analysis of synchrotron tomographic data
- (2014) Dogˇa Gürsoy et al. JOURNAL OF SYNCHROTRON RADIATION
- Iterative Reconstruction Algorithm for CT: Can Radiation Dose Be Decreased While Low-Contrast Detectability Is Preserved?
- (2013) Sebastian T. Schindera et al. RADIOLOGY
- A comparative study of X-ray tomographic microscopy on shales at different synchrotron facilities: ALS, APS and SLS
- (2012) Waruntorn Kanitpanyacharoen et al. JOURNAL OF SYNCHROTRON RADIATION
- Iterative reconstruction methods in X-ray CT
- (2012) Marcel Beister et al. Physica Medica-European Journal of Medical Physics
- Low-dose computed tomography image restoration using previous normal-dose scan
- (2011) Jianhua Ma et al. MEDICAL PHYSICS
- Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT
- (2009) Armando Manduca et al. MEDICAL PHYSICS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started