First Authors | Mangal Prakash, Manan Lalit |
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Authors | Mangal Prakash, Manan Lalit, Pavel Tomancak, Alexander Krull, Florian Jug |
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Last Authors | Florian Jug |
Conference Proceedings Volume Title | IEEE ISBI 2020 : International Conference on Biomedical Imaging : April 2-7, 2020, Iowa City, Iowa, USA : symposium proceeding |
Series Title | IEEE International Symposium on Biomedical Imaging |
Conference Name | IEEE 17th International Symposium on Biomedical Imaging (ISBI) |
Conference Location | Iowa City, Iowa, USA |
Conference Start Date | 2020-04-03 |
Conference End Date | 2020-04-07 |
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Publisher | IEEE |
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ISBN | 978-1-5386-9330-8 |
First Page | 154 |
Last Page | 158 |
Open Access | false |
Print Publication Date | 2020-05-22 |
Online Publication Date | 2020-05-22 |
Abstract | Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing. A new category of DL methods such as Noise2Void or Noise2Self can be used fully unsupervised, requiring nothing but the noisy data. However, this comes at the price of reduced reconstruction quality. The recently proposed Probabilistic Noise2Void (PN2V) improves results, but requires an additional noise model for which calibration data needs to be acquired. Here, we present improvements to PN2V that (i) replace histogram based noise models by parametric noise models, and (ii) show how suitable noise models can be created even in the absence of calibration data. This is a major step since it actually renders PN2V fully unsupervised. We demonstrate that all proposed improvements are not only academic but indeed relevant. |
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Affiliated With | CSBD, Jug, Tomancak |
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Acknowledged Services | Light Microscopy Facility |
Publication Status | Published |
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DOI | 10.1109/ISBI45749.2020.9098612 |
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Created By | thuem |
Added Date | 2021-02-03 |
Last Edited By | herbst |
Last Edited Date | 2021-06-21 17:21:07.289 |
Library ID | 7928 |
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