This repository includes the raw data download link, the python and matlab codes, as well as Fiji plugin for the paper "Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy".
For a quick start, check the brief tutorial in our website. (This website is in continuous update!)
For detailed instructions, see the ReadMe.md in the folder Python_MATLAB_Codes
or Fiji_Plugin
respectively.
Here is a 5-step hands-on guide to get you started on our Fiji plugin:
-
Copy
./jars/*
and./plugins/*
to your root path of Fiji[your root path of Fiji]/Fiji.app/
from this link, then restart Fiji. -
Open Edit > Options > Tensorflow, and choose the version matching your model or setting. After a message pops up telling you that the library was installed, restart Fiji.
-
Download one of our pre-trained_models and its test data. The corresponding test data, model type and test data type are listed in
Fiji_pretrained_models_list.xlsx
in the same folder. Open the test data in Fiji and start ZS-DeconvNet plugin by Clicking Plugins > ZS-DeconvNet > predict. -
Import the chosen model by entering the downloaded path or clicking
Browse
. ClickAdjust mapping of TF network input
and thenOK
. -
After image processing with status bar shown in the message box (if select
Show progress dialog
), the denoised (if selectShow denoising result
) and deconvolved output will pop out in separate Fiji windows automatically.