Fully Unsupervised Probabilistic Noise2Void

Autor:	Prakash, Mangal, Lalit, Manan, Tomancak, Pavel, Krull, Alexander, Jug, Florian
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at ISBI 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.12291 Zobrazit plný text záznamu View this record from Arxiv