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References (35)
-
Emily Denton, Soumith Chintala, Arthur Szlam, R. Fergus (2015)
Deep Generative Image Models using a Laplacian Pyramid of Adversarial NetworksArXiv, abs/1506.05751
-
Hu Chen, Yi Zhang, M. Kalra, Feng Lin, Yang Chen, Peixi Liao, Jiliu Zhou, Ge Wang (2017)
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural NetworkIEEE Transactions on Medical Imaging, 36
-
Christian Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, Scott Reed, Dragomir Anguelov, D. Erhan, Vincent Vanhoucke, Andrew Rabinovich (2014)
Going deeper with convolutions2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
-
Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman, Oliver Wang (2018)
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
-
K. Simonyan, Andrew Zisserman (2014)
Very Deep Convolutional Networks for Large-Scale Image RecognitionCoRR, abs/1409.1556
-
(2014)
Dictionary learning based low-dose X-ray CT reconstruction, 9212
-
B. Arnold (1986)
Noise Analysis in Digital Radiography -
Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, X. Mou, M. Kalra, Yi Zhang, Ling Sun, Ge Wang (2017)
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual LossIEEE Transactions on Medical Imaging, 37
-
I. Elbakri, J. Fessler (2002)
Statistical image reconstruction for polyenergetic X-ray computed tomographyIEEE Transactions on Medical Imaging, 21
-
B. Arnold, H. Eisenberg, D. Borger, A. Metherell (1981)
Digital Radiography: An Overview, 0273
-
Chao Dong, Chen Loy, Kaiming He, Xiaoou Tang (2014)
Image Super-Resolution Using Deep Convolutional NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 38
-
Diederik Kingma, Jimmy Ba (2014)
Adam: A Method for Stochastic OptimizationCoRR, abs/1412.6980
-
(2016)
Kwon J. Lee and K. Mu Lee, Accurate image super-resolution using very deep convolutional networks -
C. Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Aitken, A. Tejani, J. Totz, Zehan Wang, Wenzhe Shi (2016)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
-
Jiwon Kim, Jung Lee, Kyoung Lee (2015)
Accurate Image Super-Resolution Using Very Deep Convolutional Networks2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
-
Andrey Ignatov, Nikolay Kobyshev, K. Vanhoey, R. Timofte, L. Gool (2017)
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks2017 IEEE International Conference on Computer Vision (ICCV)
-
Chuan Li, Michael Wand (2016)
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
-
(2017)
Schönberg S -
Kostadin Dabov, A. Foi, V. Katkovnik, K. Egiazarian (2007)
Image Denoising by Sparse 3-D Transform-Domain Collaborative FilteringIEEE Transactions on Image Processing, 16
-
X. Mou, Junfeng Wu, T. Bai, Qiong Xu, Hengyong Yu, Ge Wang (2014)
Dictionary learning based low-dose x-ray CT reconstruction using a balancing principle, 9212
-
Eunhee Kang, Junhong Min, J. Ye (2017)
Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT ReconstructionArXiv, abs/1703.01383
-
Shuhang Gu, Lei Zhang, W. Zuo, Xiangchu Feng (2014)
Weighted Nuclear Norm Minimization with Application to Image Denoising2014 IEEE Conference on Computer Vision and Pattern Recognition
-
(2014)
Generative adversarial nets, pp -
P. Cochat, L. Vaucoret, J. Sarles (2008)
Et alEvidence Based Mental Health, 11
-
(2014)
Weighted nuclear norm minimization with application to image denoising, computer vision and pattern recognition -
J. Wang, Tianfang Li, Hongbing Lu, Zhengrong Liang (2006)
Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomographyIEEE Transactions on Medical Imaging, 25
-
J. Wolterink, T. Leiner, M. Viergever, I. Išgum (2017)
Generative Adversarial Networks for Noise Reduction in Low-Dose CTIEEE Transactions on Medical Imaging, 36
-
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2014)
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 37
-
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2015)
Deep Residual Learning for Image Recognition2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
-
Ning Zhang, Jeff Donahue, Ross Girshick, Trevor Darrell (2014)
Part-Based R-CNNs for Fine-Grained Category Detection -
A. Manduca, Lifeng Yu, J. Trzasko, N. Khaylova, J. Kofler, C. McCollough, J. Fletcher (2009)
Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT.Medical physics, 36 11
-
Yong-xia Zhao, Yanmin Wu, Ziwei Zuo, H. Suo, Sisi Zhao, Haisong Zhang (2017)
CT pulmonary angiography using different noise index values with an iterative reconstruction algorithm and dual energy CT imaging using different body mass indices: Image quality and radiation dose.Journal of X-ray science and technology, 25 1
-
F. Pisana, T. Henzler, S. Schönberg, E. Klotz, B. Schmidt, M. Kachelriess (2017)
Noise reduction and functional maps image quality improvement in dynamic CT perfusion using a new k‐means clustering guided bilateral filter (KMGB)Medical Physics, 44
-
(2016)
Kwon J -
Hu Chen, Yi Zhang, Wei-hua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang (2017)
Low-dose CT via convolutional neural network.Biomedical optics express, 8 2
- Publisher
- IOS Press
- Copyright
- Copyright © 2018 © 2018 – IOS Press and the authors. All rights reserved
- ISSN
- 0895-3996
- eISSN
- 1095-9114
- DOI
- 10.3233/XST-17356
- Publisher site
- See Article on Publisher Site
Abstract
Statistical noise may degrade the x-ray image quality of digital radiography (DR) system. This corruption can be alleviated by extending exposure time of detectors and increasing the intensity of radiation. However, in some instances, such as the security check and medical imaging examination, the system demands rapid and low-dose detection. In this study, we propose and test a generative adversarial network (GAN) based x-ray image denoising method. Images used in this study were acquired from a digital radiography (DR) imaging system. Promising results have been obtained in our experiments with x-ray images for the security check application. The Experiment results demonstrated that the proposed new image denoising method was able to effectively remove the statistical noise from x-ray images, while kept sharp edge and clear structure. Thus, comparing with the traditional convolutional neural network (CNN) based method, the proposed new method generates more plausible-looking images, which contains more details.
Journal
Journal of X-Ray Science and Technology – IOS Press
Published: Jan 1, 2018