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Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms.
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Non-strictly convex minimization over the fixed point set of an asymptotically shrinking nonexpansive mapping
Ogura, N. and Yamada, I · 2002
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A non-local algorithm for image denoising
Buades, A., Coll, B., and Morel, J.-M · 2005
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Application of compressed sensing for rapid MR imaging
Lustig, M., Santos, J. M., Lee, J.-H., Donoho, D. L., and Pauly, J. M · 2005
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Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2007
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Image deblurring by augmented Lagrangian with BM3D frame prior
Danielyan, A., Katkovnik, V., and Egiazarian, K · 2010
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A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data
Yang, J., Zhang, Y., and Yin, W · 2010
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The quanta image sensor (QIS): concepts and challenges
Fossum, E · 2011
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From learning models of natural image patches to whole image restoration
Zoran, D. and Weiss, Y · 2011
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BM3D frames and variational image deblurring
Danielyan, A., Katkovnik, V., and Egiazarian, K · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Plug-and-play priors for model based reconstruction
Venkatakrishnan, S. V., Bouman, C. A., and Wohlberg, B · 2013
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Efficient image reconstruction for gigapixel quantum image sensors
Chan, S. H. and Lu, Y. M · 2014
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FlexISP: A flexible camera image processing framework
Heide, F., Steinberger, M., Tsai, Y.-T., Rouf, M., Pająk, D., Reddy, D., Gallo, O., Liu, J., Heidrich, W., Egiazarian, K., Kautz, J., and Pulli, K · 2014
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Compositions and convex combinations of averaged nonexpansive operators
Combettes, P. L. and Yamada, I · 2015
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Single image super-resolution via BM3D sparse coding
Egiazarian, K. and Katkovnik, V · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Turning a denoiser into a super-resolver using plug and play priors
Brifman, A., Romano, Y., and Elad, M · 2016
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Postprocessing of compressed images via sequential denoising
Dar, Y., Bruckstein, A. M., Elad, M., and Giryes, R · 2016
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Decoupled algorithm for mri reconstruction using nonlocal block matching model: BM3D-MRI
Eksioglu, E. M · 2016
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Image reconstruction and threshold design for quanta image sensors
Elgendy, O. A. and Chan, S. H · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Reconnet: Non-iterative reconstruction of images from compressively sensed measurements
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., and Ashok, A · 2016
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Projected iterative soft-thresholding algorithm for tight frames in compressed sensing magnetic resonance imaging
Liu, Y., Zhan, Z., Cai, J.-F., Guo, D., Chen, Z., and Qu, X · 2016
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Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
Mao, X., Shen, C., and Yang, Y.-B · 2016
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From denoising to compressed sensing
Metzler, C. A., Maleki, A., and Baraniuk, R. G · 2016
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Poisson inverse problems by the plug-and-play scheme
Rond, A., Giryes, R., and Elad, M · 2016
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Primer on monotone operator methods
Ryu, E. K. and Boyd, S · 2016
Denoising prior driven deep neural network for image restoration
Dong, W., Wang, P., Yin, W., and Shi, G · 2018
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Plug-in estimation in high-dimensional linear inverse problems: A rigorous analysis
Fletcher, A. K., Pandit, P., Rangan, S., Sarkar, S., and Schniter, P · 2018
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Regularisation of neural networks by enforcing Lipschitz continuity
Gouk, H., Frank, E., Pfahringer, B., and Cree, M · 2018
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Optimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction
He, J., Yang, Y., Wang, Y., Zeng, D., Bian, Z., Zhang, H., Sun, J., Xu, Z., and Ma, J · 2018
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Multi-level Wavelet-CNN for image restoration
Liu, P., Zhang, H., Zhang, K., Lin, L., and Zuo, W · 2018
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Plug-and-play priors for bright field electron tomography and sparse interpolation
Sreehari, S., Venkatakrishnan, S. V., Wohlberg, B., Buzzard, G. T., Drummy, L. F., Simmons, J. P., and Bouman, C. A · 2016
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Image restoration and reconstruction using variable splitting and class-adapted image priors
Teodoro, A. M., Bioucas-Dias, J. M., and Figueiredo, M. A. T · 2016
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Deep ADMM-Net for compressive sensing MRI
Yang, Y., Sun, J., Li, H., and Xu, Z · 2016
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Spectrally-normalized margin bounds for neural networks
Bartlett, P. L., Foster, D. J., and Telgarsky, M. J · 2017
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Convex Analysis and Monotone Operator Theory in Hilbert Spaces
Bauschke, H. H. and Combettes, P. L · 2017
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Plug-and-play ADMM for image restoration: Fixed-point convergence and applications
Chan, S. H., Wang, X., and Elgendy, O. A · 2017
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Lipschitz regularized deep neural networks converge and generalize
Oberman, A. M. and Calder, J · 2018
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Neural nearest neighbors networks
Plötz, T. and Roth, S · 2018
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Plug-and-play prior based on Gaussian mixture model learning for image restoration in sensor network
Shi, M. and Feng, L · 2018
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Exact worst-case convergence rates of the proximal gradient method for composite convex minimization
Taylor, A. B., Hendrickx, J. M., and Glineur, F · 2018
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Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Tsuzuku, Y., Sato, I., and Sugiyama, M · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Weng, T.-W., Zhang, H., Chen, P.-Y., Yi, J., Su, D., Gao, Y., Hsieh, C.-J., and Daniel, L · 2018
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Deep residual learning for model-based iterative CT reconstruction using plug-and-play framework
Ye, D. H., Srivastava, S., Thibault, J., Sauer, K., and Bouman, C · 2018
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ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
Zhang, J. and Ghanem, B · 2018
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FFDNet: Toward a fast and flexible solution for cnn based image denoising
Zhang, K., Zuo, W., and Zhang, L · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Performance analysis of Plug-and-Play ADMM: A graph signal processing perspective
Chan, S. H · 2019
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InverseNet: Solving inverse problems with splitting networks
Fan, K., Wei, Q., Wang, W., Chakraborty, A., and Heller, K · 2019
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ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
Liu, J., Chen, X., Wang, Z., and Yin, W · 2019
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Iterative reconstruction for low dose ct using Plug-and-Play alternating direction method of multipliers (ADMM) framework
Lyu, Q., Ruan, D., Hoffman, J., Neph, R., McNitt-Gray, M., and Sheng, K · 2019
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L2-nonexpansive neural networks
Qian, H. and Wegman, M. N · 2019
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Regularization by denoising: Clarifications and new interpretations
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An online plug-and-play algorithm for regularized image reconstruction
Sun, Y., Wohlberg, B., and Kamilov, U. S · 2019
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A convergent image fusion algorithm using scene-adapted Gaussian-mixture-based denoising
Teodoro, A. M., Bioucas-Dias, J. M., and Figueiredo, M. A. T · 2019
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Image restoration by iterative denoising and backward projections
Tirer, T. and Giryes, R · 2019
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