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)


Utilizando Deep Learning para eliminar el ruido fotónico de Poisson de imágenes confocales de resonancia

Al usar una red neuronal convolucional que utiliza un marco MXNET codificado con varios miles de ejemplos de datos confocales de resonancia, a los datos de la imagen de entrada se les asigna pesos aprendibles, lo que resulta en el aprendizaje de la red para establecer correlaciones y reconocer patrones: el patrón principal mas común es el ruido fotónico o de Poisson; la red fue entrenada para reconocer y eliminar el ruido fotónico de conjuntos de datos del microscopio confocal A1 de resonancia. Este algoritmo entrenado de Inteligencia Artificial (AI) se puede utilizar incluso en tiempo real para eliminar el ruido.

con Denoise.AI
Original

Proyección de máxima intensidad de imagen confocal de resonancia de Danio sp. preparado por Callen Wallace y Mike Calderon, Centro de Imágenes Biológicas, Universidad de Pittsburgh para el Curso de Microscopía de Fluorescencia Cuantitativa (QFM).

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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