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Detalle de: FlowDenoising: Structure-preserving denoising in 3D electron microscopy (3DEM)

Title FlowDenoising: Structure-preserving denoising in 3D electron microscopy (3DEM)
Abstract

FlowDenoising is a software tool that implements an adaptive Gaussian denoising filter that preserves visually appreciable structures in volumes of 3D electron microscopy (3DEM). It proceeds by nonrigidly aligning the 2D slices in each dimension, using an optical flow estimator, prior to applying a standard separable (1D) Gaussian filter. FlowDenoising has been developed in Python leveraging well-known public domain libraries, such as OpenCV and NumPy. Furthermore, the software tool exploits data-level parallelism to significantly reduce processing times. Its abilities to denoise huge volumes in just minutes on standard multicore computers makes it a useful tool in 3DEM to explore the interior of cells and tissues at the nanoscale.

Apa Reference González-Ruiz, V. & Fernández, J. (2023). FlowDenoising: Structure-preserving denoising in 3D electron microscopy (3DEM). SoftwareX, 23, 101413. 10.1016/j.softx.2023.101413
Doi 10.1016/j.softx.2023.101413
Publication Date Jul 2023
Publication Type Article
Researcher Jose-Jesus Fernandez
Ontology Bioinformatics Software and Tools, Experimental structure determination, Parallel Computing, Image analysis
Research Groups
Tools

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