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The vast work in Deep Learning (DL) has led to a leap in image denoising research.
Generative adversarial nets
Ian Goodfellow et al · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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U-net: convolutional networks for biomedical image segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
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Lsun: construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu et al · 2015
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Image denoising via cnns: an adversarial approach
Nithish Divakar and R. Venkatesh Babu · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani et al · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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Adam: a method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Beyond a gaussian denoiser: residual learning of deep cnn for image denoising
Kai Zhang et al · 2017
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Jonas Adler and Ozan Öktem · 2018
Cited alongside, same era.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Are gans created equal? a large-scale study
Mario Lucic et al · 2018
Cited alongside, same era.
Ffdnet: toward a fast and flexible solution for cnn-based image denoising
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
Cited alongside, same era.
Pytorch lightning
Creating high resolution images with a latent adversarial generator
David Berthelot, Peyman Milanfar, and Ian Goodfellow · 2020
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Image denoising using generative adversarial network
Ratnadeep Dey, Debotosh Bhattacharjee, and Mita Nasipuri · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras et al · 2020
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Fast fréchet inception distance
Alexander Mathiasen and Frederik Hvilshøj · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon et al · 2020
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WA Falcon et al · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Cited alongside, same era.
State-of-art analysis of image denoising methods using convolutional neural networks
Rini Thakur, R.N. Yadav, and Lalita Gupta · 2019
Cited alongside, same era.
Diversity-sensitive conditional generative adversarial networks
Dingdong Yang et al · 2019
Cited alongside, same era.
Deep iterative down-up cnn for image denoising
Songhyun Yu, Bumjun Park, and Jechang Jeong · 2019
Cited alongside, same era.
Explorable super resolution
Yuval Bahat and Tomer Michaeli · 2020
Cited alongside, same era.
Tests for departure from normality. empirical results for the distributions of
Ralph D’Agostino and E. S. Pearson
Cited in the paper.
Maximilian Seitzer · 2020
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Deep learning on image denoising: an overview
Chunwei Tian et al · 2020
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Variational inference for computational imaging inverse problems
Francesco Tonolini et al · 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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Approximate probabilistic inference with composed flows
Jay Whang, Erik M. Lindgren, and Alexandros G. Dimakis · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song et al · 2021
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