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The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements.
Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
E. J. Candès, J. Romberg, and T. Tao · 2006
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Near-optimal signal recovery from random projections: Universal encoding strategies?
E. J. Candes and T. Tao · 2006
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Compressed sensing
D. L. Donoho · 2006
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An architecture for compressive imaging
M.B. Wakin, J.N. Laska, M.F. Duarte, D. Baron, S. Sarvotham, D. Takhar, K.F. Kelly and R.G. Baraniuk · 2006
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A new compressive imaging camera architecture using optical-domain compression
D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk · 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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Block compressed sensing of natural images
L. Gan · 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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An introduction to compressive sampling
E. J. Candes and M. B. Wakin · 2008
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Wavelet-domain compressive signal reconstruction using a hidden markov tree model
M. F. Duarte, M. B. Wakin, and R. G. Baraniuk · 2008
Earlier work this paper cites.
Non-local regularization of inverse problems
G. Peyré, S. Bougleux, and L. Cohen · 2008
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Message-passing algorithms for compressed sensing
D. L. Donoho, A. Maleki, and A. Montanari · 2009
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Model-based compressive sensing
R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde · 2010
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Compressed sensing using a gaussian scale mixtures model in wavelet domain
Y. Kim, M. S. Nadar, and A. Bilgin · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Cs-muvi: Video compressive sensing for spatial-multiplexing cameras
A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk · 2012
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Compressive imaging using approximate message passing and a markov-tree prior
S. Som and P. Schniter · 2012
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An efficient augmented lagrangian method with applications to total variation minimization
C. Li, W. Yin, H. Jiang, and Y. Zhang · 2013
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Improved total variation based image compressive sensing recovery by nonlocal regularization
Information-optimal scalable compressive imaging system
R. Kerviche, N. Zhu, and A. Ashok · 2014
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From denoising to compressed sensing
C. A. Metzler, A. Maleki, and R. G. Baraniuk · 2014
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Part-based r-cnns for fine-grained category detection
N. Zhang, J. Donahue, R. Girshick, and T. Darrell · 2014
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Panda: Pose aligned networks for deep attribute modeling
N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev · 2014
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Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao · 2013
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Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
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Compressive sensing via nonlocal low-rank regularization
W. Dong, G. Shi, X. Li, Y. Ma, and F. Huang · 2014
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Information optimal scalable compressive imager demonstrator
R. Kerviche, N. Zhu, and A. Ashok · 2014
Cited alongside, same era.
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 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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Compressive imaging via approximate message passing with image denoising
J. Tan, Y. Ma, and D. Baron · 2015
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Dense optical flow prediction from a static image
J. Walker, A. Gupta, and M. Hebert · 2015
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Designing deep networks for surface normal estimation
X. Wang, D. F. Fouhey, and A. Gupta · 2015
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Object tracking benchmark
Y. Wu, J. Lim, and M. Yang · 2015
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