there’s a new Jug-Lab Fiji plugin of Noise2Void available now (originally implemented in python). Any volunteers for testing an early version?
It’s a deep learning method for content aware denoising. Training can be done on single noisy images. This is the N2V paper: https://arxiv.org/abs/1811.10980 … And the SEM data reference book chapter, same data as in the n2v python training SEM example.
During training a loss plot and preview window is displayed to track the progress. Be aware that N2V will only remove pixel wise independent noise.
The train plugins provide you with a link to a ZIP file of the trained model which you can use to run the predict plugin on other images of the same type without retraining.
There’s a script template for batch prediction, open a new script and open
Templates > ImageJ2 > N2V > BatchPredict (python) , if you run it, it asks for a folder containing the input images, an empty folder where the predicted output images will be saved to and the model file (the ZIP from training)
For GPU support (which is kind of necessary, training with GPU still takes hours) you should have CUDA 10 and a matching cuDNN version installed. Follow the official NVIDIA guides to set the environment variables for both libraries. After adding the N2V update site, in Fiji, open
Edit > Options > TensorFlow... and install
TF 1.13.1 GPU.
Enjoy and please let us know if there are issues