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NIS-Elements Viewer

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Please contact us at NA-NII-Software_Support@nikon.com to request an installer download link by specifying the operating system.

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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