This repository provides the implementation of the paper:
Wentian Xu and Jianbo Jiao. Revisiting Implicit Neural Representations in Low-Level Vision. In ICLR-NF (2023).
We provide Jupyter Notebooks for each task in the paper. We also provide a denosing example based on .py file.
To install the dependencies into a new conda environment, simply run:
conda env create -f environment.yml
source activate linr
Alternatively, you may install them manually:
conda create --name linr
source activate linr
conda install python=3.9
conda install pytorch=1.12 torchvision cudatoolkit=11.6 -c pytorch
conda install numpy
conda install matplotlib
conda install scikit-image
conda install jupyter
- Change the file path in jupyter Notebooks or in
train_denoising.py
to the image you want - Run each cell in jupyter Notebooks or run the code in
train_denoising.py
The data used in the paper is stored in the Data folder. You can also use your own data by changing the file path.
If you find LINR useful, please cite the following BibTeX entry:
@inproceedings{linr,
title={Revisiting Implicit Neural Representations in Low-Level Vision},
author={Wentian Xu and Jianbo Jiao},
booktitle="International Conference on Learning Representations Workshop",
year={2023},
}