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Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image.
Exponential convergence of langevin distributions and their discrete approximations
Gareth O Roberts, Richard L Tweedie, et al · 1996
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Markov chain monte carlo for statistical inference
Julian Besag · 2001
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Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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A non-local algorithm for image denoising
Antoni Buades, Bartomeu Coll, and J-M Morel · 2005
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Fields of experts: A framework for learning image priors
Stefan Roth and Michael J Black · 2005
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An information fidelity criterion for image quality assessment using natural scene statistics
Hamid R Sheikh, Alan C Bovik, and Gustavo De Veciana · 2005
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Image denoising via sparse and redundant representations over learned dictionaries
Michael Elad and Michal Aharon · 2006
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Image information and visual quality
Hamid R Sheikh and Alan C Bovik · 2006
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Vsnr: A wavelet-based visual signal-to-noise ratio for natural images
Damon M Chandler and Sheila S Hemami · 2007
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Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Sparse and redundant modeling of image content using an image-signature-dictionary
Michal Aharon and Michael Elad · 2008
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Is denoising dead?
Priyam Chatterjee and Peyman Milanfar · 2009
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Mean squared error: Love it or leave it? a new look at signal fidelity measures
Zhou Wang and Alan C Bovik · 2009
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Stochastic image denoising based on markov-chain monte carlo sampling
Alexander Wong, Akshaya Mishra, Wen Zhang, Paul Fieguth, and David A Clausi · 2011
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Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity
Guoshen Yu, Guillermo Sapiro, and Stéphane Mallat · 2011
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Fsim: A feature similarity index for image quality assessment
Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang · 2011
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From learning models of natural image patches to whole image restoration
Daniel Zoran and Yair Weiss · 2011
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Patch complexity, finite pixel correlations and optimal denoising
Anat Levin, Boaz Nadler, Fredo Durand, and William T Freeman · 2012
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A nonlocal bayesian image denoising algorithm
Marc Lebrun, Antoni Buades, and Jean-Michel Morel · 2013
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Plug-and-play priors for model based reconstruction
Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
Cited alongside, same era.
Weighted nuclear norm minimization with application to image denoising
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng · 2014
Cited alongside, same era.
A review paper: Noise models in digital image processing
Ajay Boyat and Brijendra Joshi · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Cited alongside, same era.
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Class-aware fully convolutional gaussian and poisson denoising
Tal Remez, Or Litany, Raja Giryes, and Alex M Bronstein · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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Ffdnet: Toward a fast and flexible solution for cnn-based image denoising
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Deep posterior sampling: Uncertainty quantification for large scale inverse problems
Jonas Adler and Ozan Öktem · 2019
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Inverting deep generative models, one layer at a time
Qi Lei, Ajil Jalal, Inderjit S Dhillon, and Alexandros G Dimakis · 2019
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Yunjin Chen and Thomas Pock · 2016
Cited alongside, same era.
Patch ordering as a regularization for inverse problems in image processing
Gregory Vaksman, Michael Zibulevsky, and Michael Elad · 2016
Cited alongside, same era.
Image denoising by wavelet bayesian network based on map estimation
V Bhanumathi and S Lavanya · 2017
Cited alongside, same era.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2017
Cited alongside, same era.
Deep class-aware image denoising
Tal Remez, Or Litany, Raja Giryes, and Alex M Bronstein · 2017
Cited alongside, same era.
The little engine that could: Regularization by denoising (red)
Yaniv Romano, Michael Elad, and Peyman Milanfar · 2017
Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
Cited alongside, same era.
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Towards theoretically-founded learning-based denoising
Wenda Zhou and Shirin Jalali · 2019
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When and how can deep generative models be inverted?
Aviad Aberdam, Dror Simon, and Michael Elad · 2020
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Explorable super resolution
Yuval Bahat and Tomer Michaeli · 2020
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Fast mixing of multi-scale langevin dynamics underthe manifold hypothesis
Adam Block, Youssef Mroueh, Alexander Rakhlin, and Jerret Ross · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Solving linear inverse problems using the prior implicit in a denoiser
Zahra Kadkhodaie and Eero P Simoncelli · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Lidia: Lightweight learned image denoising with instance adaptation
Gregory Vaksman, Michael Elad, and Peyman Milanfar · 2020
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Residual dense network for image restoration
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2020
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