NeuroDeblur™
The Highest Performance Deconvolution SoftwareProduct Overview
NeuroDeblur is the premier deconvolution and artifact removal software for large 3D microscopy datasets. It supports a wide range of microscopy modalities, including light sheet, laser scanning confocal, spinning disc confocal, two-photon, widefield fluorescence, and brightfield. NeuroDeblur uses advanced algorithms and GPU acceleration to produce images that are clearer than the raw images obtained by the microscope.
NeuroDeblur is engineered to work with the most demanding image sets. It can be used in two ways – with an easy to use graphical user interface, or as a command-line tool for automated image processing pipelines.
Key Benefits
- Produces optimal clear deconvolved images on even the largest light sheet data sets
- Ultra-fast GPU based processing provides enormous increase in speed (>100x) compared to conventional CPU-based processing
- Excellent deconvolution results with light-sheet and confocal microscopy without the need to measure the imaging system’s point spread function (PSF)
- Stripe-artifact removal for light-sheet microscopy data
- Sophisticated adaptive image background removal algorithm
- Easy to use GUI with 3D visualization
- Micro-second precision with synchronization across multiple devices
- Powerful command-line for embedding deconvolution into complex workflows
- Works with images from most common microscope manufacturers
NeuroDeblur is the leading solution for deconvolution and artifact removal for microscopy images. It is particularly useful for working with large data sets from light sheet and confocal microscopes. With computational optimizations for the fastest performance using GPU.
NeuroDeblur is engineered by experts in microscopy and deconvolution to produce optimal results from a wide array of microscopes.
Even images from the best microscopes can be further improved using NeuroDeblur.
NeuroDeblur contains the following important features:
- Excellent deconvolution results with light sheet and confocal microscopy without or without PSF measurements
- Deconvolution using matching accurate PSF models for light sheet and confocal microscopy
- Deconvolution using a PSF measured from the imaging system
- Powerful adaptive background artifact correction
- Fast multiprocessor aware deconvolution with excellent regularizing for noise minimization
- Ultra-fast GPU based processing with modern Nvidia graphic cards (> 100 million voxels/min)
- Batch deconvolution for building an automated imaging workflow
- Automatic block-wise processing of large data sets even on computers with limited RAM
- Sophisticated stripe-artifact removal for light sheet microscopy data
- Side-by-side comparison of original and deconvolved data in synchronized display windows
- Process select ROIs and color channels of proprietary 4D- or 5D-input formats (e.g. Zeiss, Evident, Leica, etc).
- Optimize the display of deconvolved results via additive and subtractive color channel mixing.
- Image post-processing by adaptive histogram equilibration (CLAHE) and unsharp masking
Download NeuroDeblur product sheet here.
How does deconvolution software work?
Image generation by a microscope can be modeled by a numerical operation called convolution: The incident light coming from the sample is convolved with the point spread function (PSF) of the microscope to construct the image. The PSF of a fluorescence microscope can be described as the image you would get from an infinitely small and infinitely bright light-emitting point-source. In practice, it can be (approximately) measured by imaging tiny fluorescent particles that are below the resolution limit of the microscope. The PSF of an ideal microscope would just be a mathematical point. However, in any real microscope it has a certain spatial extent due to the wavelength of the illumination light and the aberrations of optical lenses. The spatial extent of the PSF inevitably causes the limited resolution and blurring observed to varying degrees in any microscopic image.
However, If the shape of the PSF of a microscope is known this process can (at least partially) be reverted by mathematically reconstructing the original shape of the sample. This process is called deconvolution:
Image = convolution (sample, PSF), sample = deconvolution (Image, PSF)
The shape of the PSF can either be measured by imaging small fluorescent particles (e.g. of 200 nm diameter) that are just below the resolution limit of the microscope, or it can be approximated using a theoretical model of the imaging process (synthetic point spread function). Both approaches are supported by NeuroDeblur.
A practical problem with any deconvolution is the potential amplification of the noise inevitably added during the imaging process by the camera and the stochastic spatial nature of photons. There are different mathematical regularization approaches to cope with this problem. The regularization approach applied by NeuroDeblur (flux preserving regularization) has the advantage of preserving the photogrammetry of data, i.e., the light intensities in distinct image regions are not shifted relatively to each other by the deconvolution process. Flux preserving regularization was originally developed for deconvolving astronomical data and to our knowledge has not been used by deconvolution software for microscopy before. Preservation of photogrammetry is especially important if quantitative comparisons of intensity are to be performed with deconvolved data. More details about the deconvolution algorithm used by NeuroDeblur in Scientific Reports, 2019 (doi.org/10.1038/s41598-019-53875-y).
Powerful Deconvolution Software for Light Sheet and Confocal Microscopy
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Cited in Peer Reviewed Scientific Publications
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