[논문]CycleGAN With a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry

Lim, Sungjun; Park, Hyoungjun; Lee, Sang-Eun; Chang, Sunghoe; Sim, Byeongsu; Ye, Jong Chul

doi:10.1109/tci.2020.3006735

[해외논문] CycleGAN With a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry 원문보기

IEEE transactions on computational imaging, v.6, 2020년, pp.1127 - 1138

Lim, Sungjun (Korea Advanced Institute of Science and Technology (KAIST), Department of Bio and Brain Engineering, Daejeon, Republic of Korea) , Park, Hyoungjun (Korea Advanced Institute of Science and Technology (KAIST), Department of Bio and Brain Engineering, Daejeon, Republic of Korea) , Lee, Sang-Eun (Seoul National University College of Medicine, Department of Physiology & Biomedical Sciences, Seoul, Republic of Korea) , Chang, Sunghoe (Seoul National University College of Medicine, Department of Physiology & Biomedical Sciences, Seoul, Republic of Korea) , Sim, Byeongsu (KAIST, Department of Mathematical Sciences, Daejeon, Republic of Korea) , Ye, Jong Chul (Korea Advanced Institute of Science and Technology (KAIST), Department of Bio and Brain Engineering, Daejeon, Republic of Korea)

Abstract ▼ AI-Helper

Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms. Recently, the convolutional neural network (CNN) approaches have been studied as a fast and high performance alternative. Unfortunately, the CNN approaches usually require matched high resolution images for supervised training. In this article, we present a novel unsupervised cycle-consistent generative adversarial network (cycleGAN) with a linear blur kernel, which can be used for both blind- and non-blind image deconvolution. In contrast to the conventional cycleGAN approaches that require two deep generators, the proposed cycleGAN approach needs only a single deep generator and a linear blur kernel, which significantly improves the robustness and efficiency of network training. We show that the proposed architecture is indeed a dual formulation of an optimal transport problem that uses a special form of the penalized least squares cost as a transport cost. Experimental results using simulated and real experimental data confirm the efficacy of the algorithm.

참고문헌 (38)

Wang, Zhou, Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.13, no.4, 600-612.

상세보기
10.1017/CBO9781139644181
KIRSHNER, H., AGUET, F., SAGE, D., UNSER, M.. 3‐D PSF fitting for fluorescence microscopy: implementation and localization application. Journal of microscopy, vol.249, no.1, 13-25.

상세보기
Sage, Daniel, Donati, Lauréne, Soulez, Ferréol, Fortun, Denis, Schmit, Guillaume, Seitz, Arne, Guiet, Romain, Vonesch, Cédric, Unser, Michael. DeconvolutionLab2: An open-source software for deconvolution microscopy. Methods, vol.115, 28-41.

상세보기
10.1109/CVPR.2017.241
10.1109/CVPR.2017.632
arXiv 1412 6980 Adam: A method for stochastic optimization kingma 2014
King, Sharon V., Yuan, Shuai, Preza, Chrysanthe. Performance evaluation of extended depth of field microscopy in the presence of spherical aberration and noise. Journal of biomedical optics, vol.23, no.3, 1-.

상세보기
Electromagn Diffraction Opt Syst II Structure of the Image Field in an Aplanatic System richards 1959 253 358
10.1007/978-3-319-66185-8_15
Zelger, P., Kaser, K., Rossboth, B., Velas, L., Schütz, G. J., Jesacher, A.. Three-dimensional localization microscopy using deep learning. Optics express, vol.26, no.25, 33166-.

상세보기
Proc Adv Neural Inf Process Syst Generative adversarial nets goodfellow 0 2672
Proc 34th Int Conf Mach Learn Wasserstein generative adversarial networks arjovsky 0 70 214
Optimal Transport Old and New villani 2008 338
Peyré, Gabriel, Cuturi, Marco. Computational Optimal Transport: With Applications to Data Science. Foundations and trends in machine learning, vol.11, no.5, 355-607.

상세보기
Nguyen, Thanh, Xue, Yujia, Li, Yunzhe, Tian, Lei, Nehmetallah, George. Deep learning approach for Fourier ptychography microscopy. Optics express, vol.26, no.20, 26470-.

상세보기
Pan, Jinshan, Dong, Jiangxin, Liu, Yang, Zhang, Jiawei, Ren, Jimmy, Tang, Jinhui, Tai, Yu-Wing, Yang, Ming-Hsuan. Physics-Based Generative Adversarial Models for Image Restoration and Beyond. IEEE transactions on pattern analysis and machine intelligence, vol.43, no.7, 2449-2462.

상세보기
10.1109/ICCV.2017.244
Kang, Eunhee, Koo, Hyun Jung, Yang, Dong Hyun, Seo, Joon Bum, Ye, Jong Chul. Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography. Medical physics, vol.46, no.2, 550-562.

상세보기
arXiv 1607 08022 Instance normalization: The missing ingredient for fast stylization ulyanov 2016
McNally, J.G., Karpova, T., Cooper, J., Conchello, J.A.. Three-Dimensional Imaging by Deconvolution Microscopy. Methods, vol.19, no.3, 373-385.

상세보기
Proc Int Conf Med Image Comput Comput -Assisted Intervention 3D U-Net: learning dense volumetric segmentation from sparse annotation çiçek 0 424
10.1007/978-3-319-10485-0_3
Chan, T.F., Wong, Chiu-Kwong. Total variation blind deconvolution. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.7, no.3, 370-375.

상세보기
10.1109/CVPR.2017.632
You, Yu-Li, Kaveh, M.. A regularization approach to joint blur identification and image restoration. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.5, no.3, 416-428.

상세보기
Rivenson, Yair, Göröcs, Zoltán, Günaydin, Harun, Zhang, Yibo, Wang, Hongda, Ozcan, Aydogan. Deep learning microscopy. Optica, vol.4, no.11, 1437-.

상세보기
Proc Adv Neural Inf Process Syst Deep convolutional neural network for image deconvolution xu 0 1790
IEEE Signal Process Mag Deconvolution methods for 3-D fluorescence microscopy images sarder 2006 10.1109/MSP.2006.1628876 23 32

상세보기
Nehme, Elias, Weiss, Lucien E., Michaeli, Tomer, Shechtman, Yoav. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica, vol.5, no.4, 458-.

상세보기
10.1007/978-3-030-33843-5_16
10.1007/978-3-030-01258-8_18
10.1109/ISBI.2019.8759250
10.1109/CVPRW.2018.00113
Schmitz, Morgan A., Heitz, Matthieu, Bonneel, Nicolas, Ngolè, Fred, Coeurjolly, David, Cuturi, Marco, Peyré, Gabriel, Starck, Jean-Luc. Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised Nonlinear Dictionary Learning. SIAM journal on imaging sciences, vol.11, no.1, 643-678.

상세보기
arXiv 1909 12116 Optimal transport, CycleGAN, and penalized LS for unsupervised learning in inverse problems sim 2019
10.1109/ICCV.2017.304
Proc Adv Neural Inf Process Syst Improved training of Wasserstein Gans gulrajani 0 5767

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