Software Resources

NIS-Elements Viewer

NIS-Elements Viewer is a free, standalone application for viewing image files and datasets with the same advanced visualization tools found in the full NIS-Elements software core packages. Enjoy consistent performance and a familiar interface, whether you're working in 2D or exploring complex 3D data.

Key Capabilities:

  • 3D Volume View for detailed rendering
  • Tile View for navigating Time, Z, and Multipoint datasets
  • Slice View for Z-stacks and time-lapse sequences
  • Full compatibility with calibrations and binary layers generated from core NIS-Elements packages
  • Access to experimental metadata, including time intervals, Z-step size, and device settings
  • Export ND datasets to TIFF with built-in tools

Download NIS-Elements Viewer User’s Guide (738 KB)

Windows Viewer

Minimum System Requirements

  • CPU Core 2 Duo or higher
  • Windows 10 Pro or later
  • 64 bit only
  • Direct X version 11 or higher

By downloading you confirm that you have read and agree with Nikon’s End User License Agreement, Terms of Use and Privacy Agreement.

Download for Windows

Mac Viewer

This is an initial release of NIS-Elements Viewer for macOS that includes a focused set of core features.

By downloading you confirm that you have read and agree with Nikon’s End User License Agreement, Terms of Use and Privacy Agreement.

Download for macOS (Apple Silicon)

Download for macOS (Intel x86_64)


Utilizing Deep Learning to remove Poisson shot noise from resonant confocal images

Using a convolutional neural network derived from MXNET encoded with several thousand examples of resonant confocal data, the input image data is assigned learnable weights and biases, 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.

See for yourself…test our deconvolution for free!

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.

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