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Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science.
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., and Tao, T · 2006
Earlier work this paper cites.
Covering an ellipsoid with equal balls
Dumer, I · 2006
Earlier work this paper cites.
An introduction to compressive sampling
Candès, E. J. and Wakin, M. B · 2008
Earlier work this paper cites.
Prior image constrained compressed sensing (piccs): a method to accurately reconstruct dynamic ct images from highly undersampled projection data sets
Chen, G.-H., Tang, J., and Leng, S · 2008
Earlier work this paper cites.
Compressed sensing MRI
Lustig, M., Donoho, D. L., Santos, J. M., and Pauly, J. M · 2008
Earlier work this paper cites.
Foundations of image science
Barrett, H. H. and Myers, K. J · 2013
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Maas, A. L., Hannun, A. Y., and Ng, A. Y · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Earlier work this paper cites.
Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Earlier work this paper cites.
Radiology Data from The Cancer Genome Atlas Glioblastoma Multiforme [TCGA-GBM] collection
Scarpace, L., Mikkelsen, T., Cha, S., Rao, S., Tekchandani, S., Gutman, D., Saltz, J. H., Erickson, B. J., Pedano, N., Flanders, A. E., Barnholtz-Sloan, J., Ostrom, Q., Barboriak, D., and Pierce, L. J · 2016
Earlier work this paper cites.
Reference-based mri
Weizman, L., Eldar, Y. C., and Ben Bashat, D · 2016
Earlier work this paper cites.
Breaking the coherence barrier: A new theory for compressed sensing
Adcock, B., Hansen, A. C., Poon, C., and Roman, B · 2017
Earlier work this paper cites.
Compressed sensing using generative models
Bora, A., Jalal, A., Price, E., and Dimakis, A. G · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al · 2017
Cited alongside, same era.
Compressed sensing with prior information: Strategies, geometry, and bounds
Mota, J. F., Deligiannis, N., and Rodrigues, M. R · 2017
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Deep video generation, prediction and completion of human action sequences
Cai, H., Bai, C., Tai, Y.-W., and Tang, C.-K · 2018
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Data from Brain-Tumor-Progression
Schmainda, K. and Prah, M · 2018
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High-dimensional probability: An introduction with applications in data science , volume 47
Vershynin, R · 2018
Cited alongside, same era.
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Asim, M., Daniels, G., Leong, O., Ahmed, A., and Hand, P · 2020
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Image-adaptive gan based reconstruction
Hussein, S. A., Tirer, T., and Giryes, R · 2020
Later among the works it cites.
Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
Later among the works it cites.
Bayesian image reconstruction using deep generative models
Marinescu, R. V., Moyer, D., and Golland, P · 2020
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Automated regularization parameter selection using continuation based proximal method for compressed sensing mri
Mathew, R. S. and Paul, J. S · 2020
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Zbontar, J., Knoll, F., Sriram, A., Muckley, M. J., Bruno, M., Defazio, A., Parente, M., Geras, K. J., Katsnelson, J., Chandarana, H., et al · 2018
Cited alongside, same era.
Separating style and content for generalized style transfer
Zhang, Y., Zhang, Y., and Cai, W · 2018
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Image2stylegan: How to embed images into the stylegan latent space?
Abdal, R., Qin, Y., and Wonka, P · 2019
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Online sequential compressed sensing with multiple information for through-the-wall radar imaging
Becquaert, M., Cristofani, E., Lauwens, B., Vandewal, M., Stiens, J. H., and Deligiannis, N · 2019
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Fast and provable admm for learning with generative priors
Gómez, F. L., Eftekhari, A., and Cevher, V · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease
LaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A., et al · 2019
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PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Menon, S., Damian, A., Hu, S., Ravi, N., and Rudin, C · 2020
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Stylerig: Rigging stylegan for 3d control over portrait images
Tewari, A., Elgharib, M., Bharaj, G., Bernard, F., Seidel, H.-P., Pérez, P., Zollhofer, M., and Theobalt, C · 2020
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Compressed sensing with invertible generative models and dependent noise
Whang, J., Lei, Q., and Dimakis, A. G · 2020
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Improving inversion and generation diversity in stylegan using a gaussianized latent space
Wulff, J. and Torralba, A · 2020
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On hallucinations in tomographic image reconstruction
Bhadra, S., Kelkar, V. A., Brooks, F. J., and Anastasio, M. A · 2021
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Intermediate layer optimization for inverse problems using deep generative models
Daras, G., Dean, J., Jalal, A., and Dimakis, A. G · 2021
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Compressible latent-space invertible networks for generative model-constrained image reconstruction
Kelkar, V. A., Bhadra, S., and Anastasio, M. A · 2021
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Standard License., 2021
Shutterstock · 2021
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