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Non-local patch based methods were until recently state-of-the-art for image denoising but are now outperformed by CNNs.
Nonlinear total variation based noise removal algorithms
L. I. Rudin, S. Osher, and E. Fatemi · 1992
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Ideal spatial adaptation by wavelet shrinkage
D. L. Donoho and J. M. Johnstone · 1994
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
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A non-local algorithm for image denoising
A. Buades, B. Coll, and J.-M. Morel · 2005
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Fields of experts: a framework for learning image priors
S. Roth and M. J. Black · 2005
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Fields of experts: a framework for learning image priors
S. Roth and M. J. Black · 2005
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Video denoising by sparse 3D transform-domain collaborative filtering
K. Dabov, A. Foi, and K. Egiazarian · 2007
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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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Training an active random field for real-time image denoising
A. Barbu · 2009
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PatchMatch
C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman · 2009
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Natural image denoising with convolutional networks
V. Jain and S. Seung · 2009
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An introduction to total variation for image analysis
V. Caselles, A. Chambolle, D. Cremers, M. Novaga, and T. Pock · 2010
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Video Denoising Using Separable 4D Nonlocal Spatiotemporal Transforms
M. Maggioni, G. Boracchi, A. Foi, and K. Egiazarian · 2011
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Learning non-local range markov random field for image restoration
J. Sun and M. F. Tappen · 2011
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From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
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Image denoising: Can plain neural networks compete with bm3d?
H. C. Burger, C. J. Schuler, and S. Harmeling · 2012
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Secrets of image denoising cuisine
M. Lebrun, M. Colom, A. Buades, and J.-M. Morel · 2012
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Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms
M. Maggioni, G. Boracchi, A. Foi, and K. Egiazarian · 2012
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A nonlocal bayesian image denoising algorithm
M. Lebrun, A. Buades, and J.-M. Morel · 2013
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Shrinkage fields for effective image restoration
U. Schmidt and S. Roth · 2014
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Towards a bayesian video denoising method
P. Arias and J.-M. Morel · 2015
Cited alongside, same era.
Bidirectional recurrent convolutional networks for multi-frame super-resolution
Y. Huang, W. Wang, and L. Wang · 2015
Cited alongside, same era.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Patch-based video denoising with optical flow estimation
A. Buades, J.-L. Lisani, and M. Miladinović · 2016
The 2017 davis challenge on video object segmentation
J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool · 2017
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Learning non-local image diffusion for image denoising
P. Qiao, Y. Dou, W. Feng, R. Li, and Y. Chen · 2017
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The little engine that could: Regularization by denoising (red)
Y. Romano, M. Elad, and P. Milanfar · 2017
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Deep video deblurring for hand-held cameras
S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich, and O. Wang · 2017
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Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
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Joint adaptive sparsity and low-rankness on the fly: an online tensor reconstruction scheme for video denoising
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Cited alongside, same era.
Deep rnns for video denoising
X. Chen, L. Song, and X. Yang · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
X. Mao, C. Shen, and Y.-B. Yang · 2016
Cited alongside, same era.
Generalized deep image to image regression
V. Santhanam, V. I. Morariu, and L. S. Davis · 2016
Cited alongside, same era.
Deep gaussian conditional random field network: A model-based deep network for discriminative denoising
R. Vemulapalli, O. Tuzel, and M. Liu · 2016
Cited alongside, same era.
Dual domain video denoising with optical flow estimation
A. Buades and J. L. Lisani · 2017
Cited alongside, same era.
B. Wen, Y. Li, L. Pfister, and Y. Bresler · 2017
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
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FFDNet: Toward a Fast and Flexible Solution for {CNN} based Image Denoising
K. Zhang, W. Zuo, and L. Zhang · 2017
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A comparison of patch-based models in video denoising
P. Arias, G. Facciolo, and J.-M. Morel · 2018
Closest in time.
Video denoising via empirical bayesian estimation of space-time patches
P. Arias and J.-M. Morel · 2018
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Learning to see in the dark
C. Chen, Q. Chen, J. Xu, and V. Koltun · 2018
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Nonlocality-reinforced convolutional neural networks for image denoising
C. Cruz, A. Foi, V. Katkovnik, and K. Egiazarian · 2018
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Non-Local Kalman: A recursive video denoising algorithm
T. Ehret, J. Morel, and P. Arias · 2018
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Non-local recurrent network for image restoration
D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang · 2018
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Burst denoising with kernel prediction networks
B. Mildenhall, J. T. Barron, J. Chen, D. Sharlet, R. Ng, and R. Carroll · 2018
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Neural nearest neighbors networks
T. Plötz and S. Roth · 2018
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Frame-Recurrent Video Super-Resolution
M. S. M. Sajjadi, R. Vemulapalli, and M. Brown · 2018
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Bm3d-net: A convolutional neural network for transform-domain collaborative filtering
D. Yang and J. Sun · 2018
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Kalman filtering of patches for frame-recursive video denoising
P. Arias and J.-M. Morel · 2019
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