Improving Blind Spot Denoising for Microscopy
- URL: http://arxiv.org/abs/2008.08414v1
- Date: Wed, 19 Aug 2020 13:06:24 GMT
- Title: Improving Blind Spot Denoising for Microscopy
- Authors: Anna S. Goncharova, Alf Honigmann, Florian Jug, Alexander Krull
- Abstract summary: We present a novel way to improve the quality of self-supervised denoising.
We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network.
- Score: 73.94017852757413
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many microscopy applications are limited by the total amount of usable light
and are consequently challenged by the resulting levels of noise in the
acquired images. This problem is often addressed via (supervised) deep learning
based denoising. Recently, by making assumptions about the noise statistics,
self-supervised methods have emerged. Such methods are trained directly on the
images that are to be denoised and do not require additional paired training
data. While achieving remarkable results, self-supervised methods can produce
high-frequency artifacts and achieve inferior results compared to supervised
approaches. Here we present a novel way to improve the quality of
self-supervised denoising. Considering that light microscopy images are usually
diffraction-limited, we propose to include this knowledge in the denoising
process. We assume the clean image to be the result of a convolution with a
point spread function (PSF) and explicitly include this operation at the end of
our neural network. As a consequence, we are able to eliminate high-frequency
artifacts and achieve self-supervised results that are very close to the ones
achieved with traditional supervised methods.
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