Software Resources
NIS-Elements Viewer
The NIS-Elements Viewer is temporarily unavailable on our website.
Please contact us at NA-NII-Software_Support@nikon.com to request an installer download link by specifying the operating system.
Denoise.ai – Trained Artificial Intelligence algorithm for confocal image denoising
Using a convolutional neural network using an MXNET framework encoded with several thousand examples of resonant confocal data, the input image data is assigned learnable weights, which results in teaching of the network to make correlations and recognize patterns: with the main common pattern being Poisson shot noise, the network was trained to recognize and remove shot noise from resonant A1 confocal data sets. This trained Artificial Intelligence (AI) algorithm can then be used even in real-time for noise removal.
With Denoise.AI
Original
Maximum intensity projection resonant confocal image of multi-labeled Danio sp. prepared by Callen Wallace and Mike Calderon, Center for Biological Imaging, University of Pittsburgh for the Quantitative Fluorescence Microscopy (QFM) Course.
Advanced 2D and 3D deconvolution algorithms for enhanced image quality
NIS-Elements offers advanced 3D and 2D deconvolution modules for improving image quality. Upload your image to our NIS-Elements deconvolution test site to see the difference.
Deconvolved
Original
Image data synchronization with an electronic lab notebook
NIS-Elements offers seamless integration for automatic upload of image metadata and thumbnails of active images directly into an eLabNext account. The NIS-Elements eLabNext Module and Add-on are freely available on eLabNext’s Marketplace.
Available with NIS-Elements version 6.10.01 or newer.
Product Brochure
Software
- NIS-Elements
