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Reconstruction of signals from compressively sensed measurements is an ill-posed problem.
Image denoising using scale mixtures of gaussians in the wavelet domain
J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli · 2003
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Fields of experts: A framework for learning image priors
S. Roth and M. J. Black · 2005
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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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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
E. J. Candès, J. Romberg, and T. Tao · 2006
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Compressed sensing
D. L. Donoho · 2006
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Compressive sensing
R. G. Baraniuk · 2007
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Deconvolution using natural image priors
A. Levin, R. Fergus, F. Durand, and W. T. Freeman · 2007
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Single-pixel imaging via compressive sampling
M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, R. G. Baraniuk, et al · 2008
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Overview of swir detectors, cameras, and applications
M. P. Hansen and D. S. Malchow · 2008
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Sparse representation for color image restoration
J. Mairal, M. Elad, and G. Sapiro · 2008
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Learning multiscale sparse representations for image and video restoration
J. Mairal, G. Sapiro, and M. Elad · 2008
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Bm3d image denoising with shape-adaptive principal component analysis
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2009
Cited alongside, same era.
Message-passing algorithms for compressed sensing
D. L. Donoho, A. Maleki, and A. Montanari · 2009
Cited alongside, same era.
From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
Cited alongside, same era.
Compressive dictionary learning for image recovery
M. Aghagolzadeh and H. Radha · 2012
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.
Neural networks
A. Graves · 2012
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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From denoising to compressed sensing
C. A. Metzler, A. Maleki, and R. G. Baraniuk · 2014
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Deep convolutional neural network for image deconvolution
L. Xu, J. S. Ren, C. Liu, and J. Jia · 2014
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Fpa-cs: Focal plane array-based compressive imaging in short-wave infrared
H. Chen, M. Salman Asif, A. C. Sankaranarayanan, and A. Veeraraghavan · 2015
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A deep learning approach to structured signal recovery
A. Mousavi, A. B. Patel, and R. G. Baraniuk · 2015
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Generative image modeling using spatial lstms
L. Theis and M. Bethge · 2015
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Cs-muvi: Video compressive sensing for spatial-multiplexing cameras
A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk · 2012
Cited alongside, same era.
Mixtures of conditional gaussian scale mixtures applied to multiscale image representations
L. Theis, R. Hosseini, and M. Bethge · 2012
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
An efficient augmented lagrangian method with applications to total variation minimization
C. Li, W. Yin, H. Jiang, and Y. Zhang · 2013
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
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Lisens-a scalable architecture for video compressive sensing
J. Wang, M. Gupta, and A. C. Sankaranarayanan · 2015
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Reconnet: Non-iterative reconstruction of images from compressively sensed measurements
K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok · 2016
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Image restoration using convolutional auto-encoders with symmetric skip connections
X.-J. Mao, C. Shen, and Y.-B. Yang · 2016
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Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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