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Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times.
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First-order methods in optimization
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“AmbientGAN: generative models from lossy measurements,”
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“Deep learning approach for Fourier ptychography microscopy,”
T. Nguyen, Y. Xue, Y. Li, L. Tian, and G. Nehmetallah, · 2018
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“Deep image prior,”
D. Ulyanov, A. Vedaldi, and V. Lempitsky, · 2018
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“Noise2Void-learning denoising from single noisy images,”
A. Krull, T.-O. Buchholz, and F. Jug, · 2019
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“Noise2Self: blind denoising by self-supervision,”
J. Batson and L. Royer, · 2019
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“Content-aware image restoration for electron microscopy,”
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“Cryo-care: content-aware image restoration for cryo-transmission electron microscopy data,”
T.-O. Buchholz, M. Jordan, G. Pigino, and F. Jug, · 2019
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“Fully unsupervised diversity denoising with convolutional variational autoencoders,”
M. Prakash, A. Krull, and F. Jug, · 2020
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“CryoGAN: a new reconstruction paradigm for single-particle Cryo-EM via deep adversarial learning,”
H. Gupta, M. T. McCann, L. Donati, and M. Unser, · 2020
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“TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion,”
Z. Liu, T. Bicer, R. Kettimuthu, D. Gursoy, F. De Carlo, and I. Foster, · 2020
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“Multi-CryoGAN: Reconstruction of continuous conformations in Cryo-EM using generative adversarial networks,”
H. Gupta, T. H. Phan, J. Yoo, and M. Unser, · 2020
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“Self-supervised learning of inverse problem solvers in medical imaging,”
O. Senouf, S. Vedula, T. Weiss, A. Bronstein, O. Michailovich, and M. Zibulevsky, · 2019
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“Real-time cryo-electron microscopy data preprocessing with warp,”
D. Tegunov and P. Cramer, · 2019
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“High-quality self-supervised deep image denoising,”
S. Laine, T. Karras, J. Lehtinen, and T. Aila, · 2019
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“Explicitly disentangling image content from translation and rotation with spatial-vae,”
T. Bepler, E. D. Zhong, K. Kelley, E. Brignole, and B. Berger, · 2019
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“Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks,”
S. Lee, S. Han, P. Salama, K. W. Dunn, and E. J. Delp, · 2019
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“Generative modeling by estimating gradients of the data distribution,”
Y. Song and S. Ermon, · 2019
Cited alongside, same era.
“Unsupervised MRI reconstruction with generative adversarial networks,”
E. K. Cole, J. M. Pauly, S. S. Vasanawala, and F. Ong, · 2020
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“Unpaired deep learning for accelerated MRI using optimal transport driven cycleGAN,”
G. Oh, B. Sim, H. Chung, L. Sunwoo, and J. C. Ye, · 2020
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“Unpaired training of deep learning tMRA for flexible spatio-temporal resolution,”
E. Cha, H. Chung, E. Y. Kim, and J. C. Ye, · 2020
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“Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network,”
E. Bostan, R. Heckel, M. Chen, M. Kellman, and L. Waller, · 2020
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“Self2self with dropout: Learning self-supervised denoising from single image,”
Y. Quan, M. Chen, T. Pang, and H. Ji, · 2020
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“Denoising diffusion probabilistic models,”
J. Ho, A. Jain, and P. Abbeel, · 2020
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“Improved simultaneous multi-slice functional MRI using self-supervised deep learning,”
O. B. Demirel, B. Yaman, L. Dowdle, S. Moeller, L. Vizioli, E. Yacoub, J. Strupp, C. A. Olman, K. Uğurbil, and M. Akçakaya, · 2021
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“An introduction to deep generative modeling,”
L. Ruthotto and E. Haber, · 2021
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“Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,”
V. Monga, Y. Li, and Y. C. Eldar, · 2021
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“Results of the 2020 fastmri challenge for machine learning mr image reconstruction,”
M. J. Muckley, B. Riemenschneider, A. Radmanesh, S. Kim, G. Jeong, J. Ko, Y. Jun, H. Shin, D. Hwang, M. Mostapha, et al., · 2021
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“Ground-truth free multi-mask self-supervised physics-guided deep learning in highly accelerated mri,”
B. Yaman, S. A. H. Hosseini, S. Moeller, J. Ellermann, K. Uğurbil, and M. Akçakaya, · 2021
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“Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data,”
H. Chung, E. Cha, L. Sunwoo, and J. C. Ye, · 2021
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“Unsupervised missing cone deep learning in optical diffraction tomography,”
H. Chung, J. Huh, G. Kim, Y. K. Park, and J. C. Ye, · 2021
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“Unsupervised content-preserving transformation for optical microscopy,”
X. Li, G. Zhang, H. Qiao, F. Bao, Y. Deng, J. Wu, Y. He, J. Yun, X. Lin, H. Xie, et al., · 2021
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“Zero-shot self-supervised learning for MRI reconstruction,”
B. Yaman, S. A. H. Hosseini, and M. Akçakaya, · 2021
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“Score-based generative modeling through stochastic differential equations,”
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, · 2021
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“Noise2score: Tweedie’s approach to self-supervised image denoising without clean images,”
K. Kim and J. C. Ye, · 2021
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“Continuous conversion of ct kernel using switchable cyclegan with adain,”
S. Yang, E. Y. Kim, and J. C. Ye, · 2021
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H. Chung and J. C. Ye, · 2021
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