N2V training in Fiji

Noise reduction (pixel wise independent) by training a CNN on single noisy images in Java.

How to use

• Add this update site to Fiji: https://sites.imagej.net/N2V
• For GPU support (Linux, Windows):
  • Install CUDA 10.0 and a matching cuDNN version. Also see OS specific notes below.
  • In Fiji, open Edit > Options > TensorFlow..., install TF 1.13.1 GPU and restart Fiji.
  • You can test if it works by running Edit > Options > TensorFlow... again - in the bottom status line it should state that the GPU TF version is active.
• Try the plugins in Plugins > CSBDeep > N2V

GPU support (Linux)

CUDA and cuDNN need to be added to your system variables in order for Fiji to be able to use them. You can do that by adding lines similar to this to your .bashrc or .zshrc file:

export PATH=/usr/local/cuda/bin${PATH}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}

.. and then launch Fiji from command line. Another possibility is to edit the Fiji launcher to something like this:

export PATH=/usr/local/cuda/bin${PATH};export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH};/PATH_TO_APPS/Fiji.app/ImageJ-linux64

GPU support (Windows)

To set the CUDA environment variables in Windows, please follow the steps described on this page (section 3.3.4).

Plugin details

• train: will take an image for training and an image for validation. If you choose the same image for both, 10% of the image will be used for validation, 90% for training. The plugin returns a window displaying the path to the zipped trained model from the last step and the zipped trained model with the lowest validation loss
• predict: takes a zipped trained model and an image; basically normalizes, calls CSBDeep, denormalizes
• train + predict: one-click solution for content aware denoising. Takes a training and a prediction image, if they are the same, 10% of the image will not be used for training, but for validation. Otherwise the prediction image is used for validation.

Credits

N2V for Fiji is written by Deborah Schmidt and Gabriella Turek.

The code is adapted from and based on N2V by Alexander Krull, Tim-Oliver Buchholz and Florian Jug:

https://arxiv.org/abs/1811.10980

Please cite this paper if you use N2V for Fiji for your research.