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DIVNOISING: Diversity Denoising with Fully Convolutional Variational Autoencoders

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Florian Jug: Ops, we did it again! Image denoising just leveled up!!! http://arxiv.org/abs/2006.06072 Our new method can do so much more than just predicting a denoised image! Let us take you on a short tour… (1/6) #CARE #denoising #uncertainty #diversity @Mangal_Prakash_ @sagzehn @jug_lab https://t.co/VMhOIWJtn6

11 replies, 377 likes


Alexander Krull: #DivNoising (http://arxiv.org/abs/2006.06072) is unsupervised denoising that generates diverse solutions, accounting for the uncertainty introduced by the noise. Now, you can use it yourself! We just published our code (https://github.com/juglab/DivNoising). Thanks to @florianjug @Mangal_Prakash_! https://t.co/1q2q3xjVDG

1 replies, 94 likes


Mangal Prakash: Ever wondered that it would be cool if image denoising methods gave you multiple plausible diverse denoised solutions for your noisy images? This would really help remove ambiguities, right? Then, check our latest preprint which uses Variational Autoencoders to do so

3 replies, 47 likes


Mangal Prakash: Our #DivNoising code is public now. Try it out, let us know how well it works for your data

1 replies, 40 likes


Alexander Krull: Why denoise your images when you can DivNoise them? Why should you settle for a single estimate when you can have the full posterior? Of course DivNoising can be trained without clean ground truth. Check it out.

0 replies, 31 likes


ʝuɢ lab: Latest lab preprint. Makes us really proud! Great team effort!!! 🎉

1 replies, 30 likes


Florian Jug: Bonus: the networks we used in the manuscript are tiny, most of them fitting in 2GB of your GPU. Hence, if you give us some time your Fiji might start talking #DivNoising to you... 😊 @FijiSc http://arxiv.org/abs/2006.06072

2 replies, 19 likes


Florian Jug: A #DivNoising network is trained to know how structures in the data look like and can be used to predict diverse denoised outputs. In areas of the image where the input is ambiguous, #DivNoising will sample the space of reasonable interpretations.(3/6) http://arxiv.org/abs/2006.06072 https://t.co/vV9UIeJ74M

2 replies, 15 likes


Florian Jug: With #DivNoising we propose a fundamentally new way to train neural networks for image denoising, using VAEs. No additional training data needed, only noisy data is enough! The required noise model can also be bootstrapped from the noisy input data… (4/6) http://arxiv.org/abs/2006.06072 https://t.co/5HnfdHyMHy

1 replies, 7 likes


Simon F. Nørrelykke: Yet another must-read from the @jug_lab: Creating a distribution of denoised images, that in turn could lead to a distribution of possible segmentations. #BioImageAnalysis #DeepLearning

0 replies, 6 likes


Alister Burt: Wow - congratulations, really looking forward to reading!

0 replies, 4 likes


Ulugbek S. Kamilov: Nice work on pushing the limits of image denoising by Florian Jug. Image denoising is fun; “Regularization by Artifact Removal” is the way to push these ideas to the whole “imaging inverse problem” world https://ieeexplore.ieee.org/document/9103213

1 replies, 3 likes


Florian Jug: Check the preprint to see that this all makes sense also from a more formal point of view. We are really excited about this and are sure that #DivNoising is powerful! We decided to demo its usefulness on an instance segmentation task. (5/6) http://arxiv.org/abs/2006.06072 https://t.co/TLd5gLHRvT

1 replies, 2 likes


Ben Engel: #DivNoise uses fully-convolutional variational autoencoders to produce diverse denoised outputs, and hopefully an improved consensus. It requires no other training data, and can bootstrap its own noise model from the noisy data 🤩. @florianjug, does it work for #cryoET data?❄️🔬

1 replies, 2 likes


Florian Jug: What always bugged us with any CARE method is that a single restored output is created, giving the false impression that the input can unambiguously be restored. But some information gets lost in the noise, so this can obviously not strictly be true! (2/6) http://arxiv.org/abs/2006.06072 https://t.co/6UOpvEBCDU

1 replies, 2 likes


Erik Olsen: Something for de-noising otolith images? @nilsolavh @Tartificiel

0 replies, 1 likes


HotComputerScience: Most popular computer science paper of the day: "DIVNOISING: Diversity Denoising with Fully Convolutional Variational Autoencoders" https://hotcomputerscience.com/paper/divnoising-diversity-denoising-with-fully-convolutional-variational-autoencoders https://twitter.com/florianjug/status/1271396999775096832

0 replies, 1 likes


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Found on Jun 12 2020 at https://arxiv.org/pdf/2006.06072.pdf

PDF content of a computer science paper: DIVNOISING: Diversity Denoising with Fully Convolutional Variational Autoencoders