napari sairyscan


Airyscan image reconstruction

This plugin implements various methods to reconstruct high resolution images for the Airyscan microscope raw data


Airyscan raw images are confocal images obtained from 32 sub-detectors. Several methods can be used to reconstruct a high resolution image from these 32 sub-detectors images. This plugin implements the following methods:

  • Pseudo-confocal: creates a pseudo confocal image by summing 7, 19 or 19 detectors
  • ISM: creates a higher resolution image by summing the images from the 32 sub-detectors after co-registering all the images to the central detector. A deconvolution algorithm can be applied in post-processing to gain more resolution
  • IFED: reconstructs a high resolution image by subtracting the outer ring detector to the central detector. This method can be interpreted as a 'virtual STED'
  • ISFED: reconstructs a high resolution image by combining the co-registered detectors images and the raw detectors images
  • Join deconvolution: reconstructs a high resolution image by jointly deblurring all 32 detectors with a variational approach.

Example image

Intended Audience & Supported Data

Supported data are raw images from the Airyscan microscope. These images must be stacks of 32 layers corresponding to the 32 detectors. The Airyscan reader plugin can open .czi raw files. Data can also be stored in any format that napari can open.


  • Open the sample image from the menu File > Open samples > napari-sairyscan > SAiryscan

Open image Open image

  • Open the SAiryscan plugin from the menu Plugins > napari-sairyscan: Airyscan reconstruction

Open image Open image

  • We select the join deconvolution method and run it with the default parameters. Default parameters are optimized for the sample image:

Open image

Getting Help

For any bug report or feature request please file an issue

How to Cite

If you use this plugin please cite the paper:

author={Prigent, Sylvain and Dutertre, Stephanie and Kervrann, Charles},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
title={Empirical Sure-Guided Microscopy Super-Resolution Image Reconstruction from Confocal Multi-Array Detectors}, 


  • 0.0.2

Last updated:

  • 07 June 2022

First released:

  • 03 June 2022


Supported data:

  • Information not submitted

Open extension:

GitHub activity:

  • Stars: 1
  • Forks: 0
  • Issues + PRs: 0

Python versions supported:

Operating system:


  • numpy
  • magicgui
  • qtpy
  • sairyscan (>=0.0.2)

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