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Image denoising and artefact removal are complex inverse problems admitting multiple valid solutions.
Fully unsupervised probabilistic noise2void
Mangal Prakash, Manan Lalit, Pavel Tomancak, Alexander Krull, and Florian Jug · 1911
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An introduction to variational autoencoders
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Nonlinear total variation based noise removal algorithms
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Gradient-based learning applied to document recognition
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Tikhonov regularization and total least squares
Gene H Golub, Per Christian Hansen, and Dianne P O’Leary · 1999
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The beetle
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A non-local algorithm for image denoising
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Fields of experts: A framework for learning image priors
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From sparse solutions of systems of equations to sparse modeling of signals and images
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A tour of modern image filtering: New insights and methods, both practical and theoretical
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Diederik P. Kingma and Max Welling · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Diederik P Kingma and Jimmy Ba · 2015
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Improving variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Tim-Oliver Buchholz, Mareike Jordan, Gaia Pigino, and Florian Jug · 2019
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Noise2void-learning denoising from single noisy images
Alexander Krull, Tim-Oliver Buchholz, and Florian Jug · 2019
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High-quality self-supervised deep image denoising
Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila · 2019
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Towards hierarchical discrete variational autoencoders
Valentin Liévin, Andrea Dittadi, Lars Maaløe, and Ole Winther · 2019
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Biva: A very deep hierarchy of latent variables for generative modeling
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Removing structured noise with self-supervised blind-spot networks
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Denoiseg: Joint denoising and segmentation
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Projected distribution loss for image enhancement
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Improving blind spot denoising for microscopy
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Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Alexander Krull, Tomas Vicar, Mangal Prakash, Manan Lalit, and Florian Jug · 2020
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Self2self with dropout: Learning self-supervised denoising from single image
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Nvae: A deep hierarchical variational autoencoder
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Noise2same: Optimizing a self-supervised bound for image denoising
Yaochen Xie, Zhengyang Wang, and Shuiwang Ji · 2020
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Very deep {vae}s generalize autoregressive models and can outperform them on images
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