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.

Keywords

• Gaussian denoising
• Noise filtering
• Optical flow
• 3D electron microscopy
• FIB-SEM
• CryoET