/pi-ddpm

显微镜模糊重建

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Physics Informed Denoising Diffusion Probabilistic Model

This is the implementation of the PI-DDPM network using a basic UNet architecture as backbone.

How to Train

  • To train the model place your data generated by the dataset_generation script (if you are generating simulated data) or the STORM script if you are generating the respective STORM dataset.
  • In the train_ddpm or train_unet script change the paths of the loading data to the ones that you generated.
  • Choose a training modality, either widefield or confocal.
  • Run the script.

How to Test

  • To test the model generate your testing dataset using the dataset_generation script.
  • Change the paths corresponding to your data.
  • Change the paths to the weights files that you wish to use.
  • Run the test script.