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Compressive sensing is a method to recover the original image from undersampled measurements.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
E. J. Candes, J. Romberg, and T. Tao · 2006
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Compressed sensing
D. L. Donoho · 2006
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Image denoising by sparse 3-d transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2007
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Sparse MRI: The application of compressed sensing for rapid MR imaging
M. Lustig, D. Donoho, and J. M. Pauly · 2007
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Monte-Carlo SURE: A black-box optimization of regularization parameters for general denoising algorithms
S. Ramani, T. Blu, and M. Unser · 2008
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
A. Beck and M. Teboulle · 2009
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Message-passing algorithms for compressed sensing
D. L. Donoho, A. Maleki, and A. Montanari · 2009
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Natural image denoising with convolutional networks
V. Jain and S. Seung · 2009
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UserÕs guide for TVAL3: TV minimization by augmented Lagrangian and alternating direction algorithms
C. Li, W. Yin, and Y. Zhang · 2009
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Compressed sensing imaging techniques for radio interferometry
Y. Wiaux, L. Jacques, G. Puy, A. M. M. Scaife, and P. Vandergheynst · 2009
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Compressed sensing based cone-beam computed tomography reconstruction with a first-order method
K. Choi, J. Wang, L. Zhu, T.-S. Suh, S. Boyd, and L. Xing · 2010
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Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
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Sparsity and Compressed Sensing in Radar Imaging
L. C. Potter, E. Ertin, J. T. Parker, and M. Cetin · 2010
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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. A. Manzagol · 2010
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MR image reconstruction from highly undersampled k-space data by dictionary learning
S. Ravishankar and Y. Bresler · 2011
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Alternating Direction Algorithms for
J. Yang and Y. Zhang · 2011
Cited alongside, same era.
Image denoising: Can plain neural networks compete with BM3D?
H. C. Burger, C. J. Schuler, and S. Harmeling · 2012
Cited alongside, same era.
Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
Cited alongside, same era.
Compressive Coded Aperture Spectral Imaging: An Introduction
G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle · 2013
Cited alongside, same era.
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection
C.-A. Deledalle, S. Vaiter, J. Fadili, and G. Peyré · 2014
Cited alongside, same era.
Compressive sensing via nonlocal low-rank regularization
W. Dong, G. Shi, X. Li, Y. Ma, and F. Huang · 2014
Cited alongside, same era.
Deep ADMM-Net for compressive sensing MRI
Y. Yang, J. Sun, H. Li, and Z. Xu · 2016
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NTIRE 2017 challenge on single image super-resolution: Dataset and study
E. Agustsson and R. Timofte · 2017
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Compressed sensing using generative models
A. Bora, A. Jalal, E. Price, and A. G. Dimakis · 2017
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AMP-inspired deep networks for sparse linear inverse problems
M. Borgerding, P. Schniter, and S. Rangan · 2017
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Learning a variational network for reconstruction of accelerated MRI data
K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll · 2017
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Deep Convolutional Neural Network for Inverse Problems in Imaging
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser · 2017
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Low-cost compressive sensing for color video and depth
X. Yuan, P. Llull, X. Liao, J. Yang, D. J. Brady, G. Sapiro, and L. Carin · 2014
Cited alongside, same era.
Phase retrieval from coded diffraction patterns
E. J. Candes, X. Li, and M. Soltanolkotabi · 2015
Cited alongside, same era.
ADAM: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
BM3D-AMP: A new image recovery algorithm based on BM3D denoising
C. A. Metzler, A. Maleki, and R. G. Baraniuk · 2015
Cited alongside, same era.
Hyperspectral compressive sensing using manifold-structured sparsity prior
L. Zhang, W. Wei, Y. Zhang, F. Li, C. Shen, and Q. Shi · 2015
Cited alongside, same era.
Learned D-AMP: Principled neural network based compressive image recovery
C. A. Metzler, A. Maleki, and R. G. Baraniuk · 2017
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Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
Later among the works it cites.
AmbientGAN: Generative models from lossy measurements
A. Bora, E. Price, and A. G. Dimakis · 2018
Closest in time.
Denoising AMP for MRI reconstruction: BM3D-AMP-MRI
E. M. Eksioglu and A. K. Tanc · 2018
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CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser · 2018
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Noise2Noise: Learning image restoration without clean data
J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila · 2018
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Training deep learning based denoisers without ground truth data
S. Soltanayev and S. Y. Chun · 2018
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LAPRAN: A scalable Laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction
K. Xu, Z. Zhang, and F. Ren · 2018
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ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
J. Zhang and B. Ghanem · 2018
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