JANNI implements a neural network denoising tool described in NVIDIA's noise2noise paper: Noise2Noise: Learning Image Restoration without Clean Data - arXiv

It can be trained on your data without the need of ground truth images. It supports MRC and TIFF format.

JANNI can be used a command line tool but also provides an simple interface to integrate into other programs (see developer information).

• License: MIT
• GitHub repository: https://github.com/thorstenwagner/sphire-janni

This is an example where we applied JANNI:

Version: 0.0.4

Uploaded: 09. July 2019

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Version: 20190703 (Trained on 1xFalcon 2, 3x Falcon 3, 7xK2 datasets from Arctica / Krios)

Uploaded: 03. July 2019

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- Issue 0 (Version 0.0.1): On some machines (maybe related to ubuntu 16.04) JANNI complains that imagecodecs module (module for tiff files) cannot be found. However, imagecodecs-lite is installed and the error also occurs if you try to denoise MRC. pip uninstall imagecodecs-lite helps.

The following instructions assume that pip and anaconda or miniconda are available. In case you have crYOLO installed, you can also install it into the crYOLO environment. In this case you can skip step 1-3.

1. In case you have a old JANNI environment installed, you might want to remove the old one with:

conda env remove --name janni

2. After that, create a new virtual environment:

conda create -n janni -c anaconda python=3.6 cudnn=7.1.2 libtiff

3. Activate the environment:

source activate janni

4. Install JANNI form PyPi:

pip install janni[gpu]

Here you can find a tutorial how to denoise your microgragh with JANNI