In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution simultaneously fuses and deconvolves the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline.
Restores images from microscopic data. Huygens is based on the deconvolution approach that reassigns out-of-focus light to its origin, thus improves signal-to-noise in images. It can use physically-acquired or simulated point-spread functions (PSFs) for characterization of optical system being deconvolved. The tool shows high-performance in in-house tests on deconvolution compared to other software packages. It provides intuitive wizards for parameter selection and processing.
An image fusion plugin that computes one single image from several three-dimensional (3d) acquisitions (views) of the same specimen, taken in different orientations. The deconvolution tries to estimate the most probable image that best explains all views, given the individual point spread function (PSF) of each view. It can be computed for single timepoints or an entire time series.
An automated workflow for processing large multiview, multi-channel, multi-illumination time-lapse selective plane illumination microscopy (SPIM) data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it.
Performs multi-view (MV) deconvolution plane-wise, which reduces memory requirements compared with existing methods and thus permits an entirely GPU-based implementation. The achieved acceleration makes MV deconvolution for the first time applicable in real-time without the need for data cropping or resampling.