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With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones.
Hornik, K., Stinchcombe, M., White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks
1989
Earlier work this paper cites.
Wallace, G. K. (1992). The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics
1992
Earlier work this paper cites.
Martin, D., Fowlkes, C., Tal, D., Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. ICCV
2001
Earlier work this paper cites.
Candes, E. J and Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies?. IEEE Transactions on Information Theory
2006
Earlier work this paper cites.
Lustig, M., Donoho, D., Pauly, J. M. (2007). Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine
2007
Earlier work this paper cites.
Duarte, M. F., Davenport, M. A., Takbar, D., Laska, J. N., Sun, T., Kelly, K. F., and Baraniuk, R. G. (2008). Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine
2008
Earlier work this paper cites.
Mun, S., Fowler, J. E. (2009). Block compressed sensing of images using directional transforms. IEEE ICIP
2009
Earlier work this paper cites.
Beck, A., Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on Imaging Sciences
2009
Earlier work this paper cites.
He, L., Carin, L. (2009). Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Transactions on Signal Processing, 57(9), 3488-3497
2009
Earlier work this paper cites.
Kim, Y., Nadar, M. S., Bilgin, A. (2010). Compressed sensing using a Gaussian scale mixtures model in wavelet domain. IEEE ICIP
2010
Earlier work this paper cites.
Gregor, K., LeCun, Y. (2010). Learning fast approximations of sparse coding. ICML
2010
Earlier work this paper cites.
Afonso, M. V., Bioucas-Dias, J. M., Figueiredo, M. A. (2011). An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Transactions on Image Processing
2011
Earlier work this paper cites.
Zhang, J., Zhao, D., Zhao, C., Xiong, R., Ma, S., Gao, W. (2012). Image compressive sensing recovery via collaborative sparsity. IEEE Journal on Emerging and Selected Topics in Circuits and Systems
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. NIPS
2012
Earlier work this paper cites.
Sankaranarayanan, A. C., Studer, C., Baraniuk, R. G. (2012). CS-MUVI: Video compressive sensing for spatial-multiplexing cameras. ICCP
2012
Earlier work this paper cites.
Sullivan, G. J., Ohm, J., Han, W. J., Wiegand, T. (2012). Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on Circuits and Systems for Vdeo Technology
2012
Earlier work this paper cites.
Xie, J., Xu, L., Chen, E. (2012). Image denoising and inpainting with deep neural networks. NIPS
2012
Earlier work this paper cites.
Li, C., Yin, W., Jiang, H., Zhang, Y. (2013). An efficient augmented Lagrangian method with applications to total variation minimization. Computational Optimization and Applications
2013
Earlier work this paper cites.
Zhang, Z., Jung, T. P., Makeig, S., Rao, B. D. (2013). Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning. IEEE Transactions on Biomedical Engineering
2013
Cited alongside, same era.
Zhang, J., Zhao, D., Jiang, F., Gao, W. (2013). Structural group sparse representation for image compressive sensing recovery. IEEE Data Compression Conference (DCC)
2013
Cited alongside, same era.
Dong, W., Shi, G., Li, X., Ma, Y., Huang, F. (2014). Compressive sensing via nonlocal low-rank regularization. IEEE Transactions on Image Processing
2014
Cited alongside, same era.
Dong, C., Loy, C. C., He, K., Tang, X. (2014). Learning a deep convolutional network for image super-resolution. ECCV
2014
Cited alongside, same era.
Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint
He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. CVPR
2016
Later among the works it cites.
2016
Later among the works it cites.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … Kudlur, M. (2016). TensorFlow: A System for Large-Scale Machine Learning. OSDI
2016
Later among the works it cites.
Liu, D., Wang, Z., Wen, B., Yang, J., Han, W., Huang, T. S. (2016). Robust single image super-resolution via deep networks with sparse prior. IEEE Transactions on Image Processing
2016
Later among the works it cites.
Wang, Z., Liu, D., Chang, S., Ling, Q., Yang, Y., Huang, T. S. (2016). D3: Deep dual-domain based fast restoration of JPEG-compressed images. CVPR
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2014
Cited alongside, same era.
Zhang, J., Zhao, D., Gao, W. (2014). Group-based sparse representation for image restoration. IEEE Transactions on Image Processing, 23(8), 3336-3351
2014
Cited alongside, same era.
Schmidt, U., Roth, S. (2014). Shrinkage fields for effective image restoration. CVPR
2014
Cited alongside, same era.
Liutkus, A., Martina, D., Popoff, S., Chardon, G., Katz, O., Lerosey, G., … Carron, I. (2014). Imaging with nature: Compressive imaging using a multiply scattering medium. Scientific Reports
2014
Cited alongside, same era.
Zhang, J., Zhao, C., Zhao, D., Gao, W. (2014). Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization. Signal Processing
2014
Cited alongside, same era.
Mousavi, A., Patel, A. B., Baraniuk, R. G. (2015, September). A deep learning approach to structured signal recovery. Annual Allerton Conference on Communication, Control, and Computing
2015
Cited alongside, same era.
Long, J., Shelhamer, E., Darrell, T. (2015). Fully convolutional networks for semantic segmentation. CVPR
2015
Cited alongside, same era.
Chen, Y., Yu, W., Pock, T. (2015). On learning optimized reaction diffusion processes for effective image restoration. CVPR
2015
Cited alongside, same era.
2016
Later among the works it cites.
Wang, S., Fidler, S., Urtasun, R. (2016). Proximal deep structured models. NIPS
2016
Later among the works it cites.
Xin, B., Wang, Y., Gao, W., Wipf, D., Wang, B. (2016). Maximal sparsity with deep networks?. NIPS
2016
Later among the works it cites.
Riegler, G., R¨¹ther, M., Bischof, H. (2016). ATGV-net: Accurate depth super-resolution. ECCV
2016
Later among the works it cites.
Zhao, C., Zhang, J., Ma, S., Gao, W. (2016). Nonconvex Lp nuclear norm based ADMM framework for compressed sensing. IEEE Data Compression Conference (DCC)
2016
Later among the works it cites.
Borgerding, M., Schniter, P., Rangan, S. (2017). AMP-Inspired Deep Networks for Sparse Linear Inverse Problems. IEEE Transactions on Signal Processing
2017
Closest in time.
Rousset, F., Ducros, N., Farina, A., Valentini, G., D¡¯Andrea, C., Peyrin, F. (2017). Adaptive basis scan by wavelet prediction for single-pixel imaging. IEEE Transactions on Computational Imaging
2017
Closest in time.
Zhang, K., Zuo, W., Gu, S., Zhang, L. (2017). Learning deep CNN denoiser prior for image restoration. CVPR
2017
Closest in time.
Rick Chang, J. H., Li, C. L., Poczos, B., Vijaya Kumar, B. V. K., Sankaranarayanan, A. C. (2017). One Network to Solve Them All–Solving Linear Inverse Problems Using Deep Projection Models. ICCV
2017
Closest in time.
Mousavi, A., Baraniuk, R. G. (2017). Learning to invert: Signal recovery via deep convolutional networks. ICASSP
2017
Closest in time.
Jin, K. H., McCann, M. T., Froustey, E., Unser, M. (2017). Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing
2017
Closest in time.
Zhao, C., Ma, S., Zhang, J., Xiong, R., Gao, W. (2017). Video compressive sensing reconstruction via reweighted residual sparsity. IEEE Transactions on Circuits and Systems for Video Technology
2017
Closest in time.
Iliadis, M., Spinoulas, L., Katsaggelos, A. K. (2018). Deep fully-connected networks for video compressive sensing. Digital Signal Processing
2018
Closest in time.