ZS-DeconvNet

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. Click Adjust mapping of TF network input and then OK.

• After image processing with status bar shown in the message box (if select Show progress dialog), the denoised (if select Show denoising result) and deconvolved output will pop out in separate Fiji windows automatically.