ArticleBeilstein journal of nanotechnology2014
Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy.
Article in Beilstein journal of nanotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 9 citations in OpenAlex.
- Autofluorescence multiphoton microscopy for visualization of tissue morphology and cellular dynamics in murine and human airways.Laboratory investigation; a journal of technical methods and pathology · 2016Article
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10 authors at 2 institutions in 1 country.
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Abstract
Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.
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