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).
This is an example where we applied JANNI:
Uploaded: 25. July 2022
Version: 20190703 (Trained on 1xFalcon 2, 3x Falcon 3, 7xK2 datasets from Arctica / Krios)
Uploaded: 03. July 2019
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.
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 conda-forge -c anaconda pyqt=5 python=3.7 cudatoolkit=10.0.130 cudnn=7.6.5 numpy==1.18.5 libtiff wxPython=4.1.1 adwaita-icon-theme
3. Activate the environment:
source activate janni
Now can install JANNI either for GPUs or for CPUs:
4a. For GPU: Install JANNI form PyPi for a GPU machine:
pip install janni[gpu]
4b. For CPU: Install JANNI form PyPi for a CPU machine:
pip install janni[cpu]
Sony Malhotra) .