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Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix.
“Atomic decomposition by basis pursuit,”
S. S. Chen, D. L. Donoho, and M. A. Saunders, · 1999
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“Compressed sensing,”
D. L. Donoho, · 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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“Block compressed sensing of natural images,”
L. Gan, · 2007
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“Optimized projections for compressed sensing,”
M. Elad, · 2007
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“Deterministic constructions of compressed sensing matrices,”
R. A. DeVore, · 2007
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“Compressed sensing of audio signals using multiple sensors,”
A. Griffin and P. Tsakalides, · 2008
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“A simple proof of the restricted isometry property for random matrices,”
R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, · 2008
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“Labelme: A database and web-based tool for image annotation,”
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, · 2008
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“Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery,”
L. Applebaum, S. D. Howard, S. Searle, and R. Calderbank, · 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
Earlier work this paper cites.
“Toeplitz compressed sensing matrices with applications to sparse channel estimation,”
J. D. Haupt, W. U. Bajwa, G. Raz, and R. Nowak, · 2010
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“Bayesian compressive sensing via belief propagation,”
D. Baron, S. Sarvotham, and R. G. Baraniuk, · 2010
Cited alongside, same era.
“Deterministic construction of Binary, Bipolar and Ternary compressed sensing matrices,”
A. Amini and F. Marvasti, · 2011
Cited alongside, same era.
“Deep sparse rectifier neural networks,”
X. Glorot, A. Bordes, and Y. Bengio, · 2011
Cited alongside, same era.
“Construction of Incoherent Unit Norm Tight Frames with Application to Compressed Sensing,”
E. Tsiligianni, L. P. Kondi, and A. K. Katsaggelos, · 2014
Cited alongside, same era.
“Deterministic construction of sparse sensing matrices via finite geometry,”
S. Li and G. Ge, · 2014
Cited alongside, same era.
“A deep learning approach to structured signal recovery,”
A. Mousavi, A. B. Patel, and R. G. Baraniuk, · 2015
“Adaptive-rate reconstruction of time-varying signals with application in compressive foreground extraction,”
J. F. C. Mota, N. Deligiannis, A. C. Sankaranarayanan, V. Cevher, and M. R. D. Rodrigues, · 2016
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“Image super-resolution using deep convolutional networks,”
C. Dong, C. C. Loy, K. He, and X. Tang, · 2016
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“A deep learning approach to block-based compressed sensing of images,”
A. Adler, D. Boublil, M. Elad, and M. Zibulevsky, · 2016
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“Exploiting correlations among channels in distributed compressive sensing with convolutional deep stacking networks,”
H. Palangi, R. Ward, and L. Deng, · 2016
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“A deep learning framework of quantized compressed sensing for wireless neural recording,”
B. Sun, H. Feng, K. Chen, and X. Zhu, · 2016
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Cited alongside, same era.
“Binaryconnect: Training deep neural networks with binary weights during propagations,”
M. Courbariaux, Y. Bengio, and J-P. David, · 2015
Cited alongside, same era.
“Learning both weights and connections for efficient neural networks,”
S. Han, J. Pool, J. Tran, and W. J. Dally, · 2015
Cited alongside, same era.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
S. Ioffe and C. Szegedy, · 2015
Cited alongside, same era.
“Imagenet large scale visual recognition challenge,”
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, · 2015
Cited alongside, same era.
“Adam: A method for stochastic optimization,”
D. P. Kingma and J. L. Ba, · 2015
Cited alongside, same era.
M. Iliadis, L. Spinoulas, and A. K. Katsaggelos, · 2016
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“Measurement matrix design for compressive sensing with side information at the encoder,”
P. Song, J. F. C. Mota, N. Deligiannis, and M. R. D. Rodrigues, · 2016
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“Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,”
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, · 2016
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“XNOR-net: Imagenet classification using binary convolutional neural networks,”
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, · 2016
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“Deep compression - compressing deep neural networks with pruning, trained quantization and huffman coding,”
S. Han, H. Mao, and W. J. Dally, · 2016
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“Compressed sensing with prior information: Strategies, geometry, and bounds,”
J. F. C. Mota, N. Deligiannis, and M. R. D. Rodrigues, · 2017
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