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NanoJ-SQUIRREL - ImageJ Plugin

The SQUIRREL paper is available in Nature Methods and BioRXiV.

20/03/19 - A beta version of the new SQUIRREL optimiser, UNIQORN!, has just been released! Please have a look at the accompanying release note (PDF, 0.3MB)

SQUIRREL is an analytical approach for quantifying image quality in super-resolution microscopy, provided as a GPU-enabled open-source ImageJ plugin. SQUIRREL requires two input images - a super-resolution image (or image stack) and the diffraction-limited equivalent of the same imaging volume. It then calculates an error-map, highlighting areas of the super-resolution image which exhibit poor agreement with the diffraction-limited image, and quality metrics for the super-resolution image.

squirrel logo.png

Features of SQUIRREL

  • Generation of error maps and quality metrics for assessment of super-resolution images
  • Option to fuse different super-resolution renderings of the same structure to minimise overall error contributions
  • FRC (Fourier Ring Correlation) mapping of resolution across super-resolution images

Getting started with SQUIRREL

There is a detailed manual explaining how to install and run SQUIRREL available here (PDF, 10.4MB).

Example data for running SQUIRREL

Here's some example data to test the algorithm (described fully in the manual):

  • For error mapping: A simulated SR dataset (.zip, 57.8MB) where we've added defects.
  • For error mapping: A real SR dataset (.zip, 69.9MB) acquired at different illumination intensities
  • For FRC resolution calculation: A couple of SMLM datasets (.zip, 13.5MB) already split into 2 independent frames each
  • For error mapping and image fusion: A real SR dataset (.zip, 35.8MB) reconstructed via three different methods and provided with corresponding error maps.