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Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafted image priors such as sparsity and low-rank.
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Atomic decomposition by basis pursuit
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Image denoising with block-matching and 3d filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2006
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Compressed sensing
D. Donoho · 2006
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K-svd: An algorithm for designing overcomplete dictionaries for sparse representation
M. Aharon, M. Elad, and A. Bruckstein · 2006
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Improved approximation algorithms for large matrices via random projections
Tamás S · 2006
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D. Needell and J. Tropp · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol · 2010
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Model-based compressive sensing
R. Baraniuk, V. Cevher, M. Duarte, and C. Hegde · 2010
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Solving random quadratic systems of equations is nearly as easy as solving linear systems
Y. Chen and E. Candes · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Image super-resolution using deep convolutional networks
C. Dong, C. Loy, K. He, and X. Tang · 2016
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Optimal rates of convergence for noisy sparse phase retrieval via thresholded wirtinger flow
T. Cai, X. Li, and Z. Ma · 2016
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Reshaped wirtinger flow for solving quadratic system of equations
H. Zhang and Y. Liang · 2016
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Reshaped wirtinger flow for solving quadratic system of equations
Huishuai Zhang and Yingbin Liang · 2016
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One network to solve them all—solving linear inverse problems using deep projection models
J. Chang, C. Li, B. Póczos, and B. Kumar · 2017
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Compressed sensing using generative models
A. Bora, A. Jalal, E. Price, and A. Dimakis · 2017
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Convolutional dictionary learning via local processing
V. Papyan, Y. Romano, J. Sulam, and M. Elad · 2017
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Multilayer convolutional sparse modeling: Pursuit and dictionary learning
J. Sulam, V. Papyan, Y. Romano, and M. Elad · 2018
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Compressed sensing with deep image prior and learned regularization
D. Van Veen, A. Jalal, E. Price, S. Vishwanath, and A. Dimakis · 2018
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Solving linear inverse problems using gan priors: An algorithm with provable guarantees
V. Shah and C. Hegde · 2018
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Gradient descent provably optimizes over-parameterized neural networks
S. Du, X. Zhai, B. Poczos, and A. Singh · 2018
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T. Lillicrap Y. Wu, M. Rosca · 2019
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Fast, sample-efficient algorithms for structured phase retrieval
G. Jagatap and C. Hegde · 2017
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Sparse phase retrieval via truncated amplitude flow
G. Wang, L. Zhang, G. Giannakis, M. Akçakaya, and J. Chen · 2017
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prdeep: Robust phase retrieval with a flexible deep network
C. Metzler, P. Schniter, A. Veeraraghavan, and R. Baraniuk · 2018
Cited alongside, same era.
Phase retrieval under a generative prior
P. Hand, O. Leong, and V. Voroninski · 2018
Cited alongside, same era.
Deep image prior
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2018
Cited alongside, same era.
Deep decoder: Concise image representations from untrained non-convolutional networks
R. Heckel and P. Hand · 2018
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A. Dimakis S. Ravula · 2019
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Audio denoising with deep network priors
L. Wolf M. Michelashvili · 2019
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Deep ptych: Subsampled fourier ptychography using generative priors
F. Shamshad, F. Abbas, and A. Ahmed · 2019
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Regularizing linear inverse problems with convolutional neural networks
R. Heckel · 2019
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R. Hyder, V. Shah, C. Hegde, and S. Asif · 2019
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Sample-efficient algorithms for recovering structured signals from magnitude-only measurements
G. Jagatap and C. Hegde · 2019
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S. Oymak and M. Soltanolkotabi · 2019
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