DINER: Disorder-Invariant Implicit Neural Representation

PyTorch implementation of DINER.

Pipeline

Setup

We provide a conda environment setup file including all of the above dependencies. Create the conda environment DINER by running:

conda create -n diner python=3.9
conda activate diner
pip install -r requirements.txt

Training

Image Representation

For tasks like fitting a single image, we prepare a test image in the data folder.

To train image representations, use the config files in the config folder. For example, to train on the provided image, run the following

python train_img.py --config ./config/img.ini

After the image representation has been trained, the results of the image will appear in the log/<experiment_name> folder, where <experiment_name> is the subdirectory in the log folder corresponding to the particular training run.

Lensless imaging

python train_lensless.py --config ./config/lensless.ini

Citation

@inproceedings{xie2023diner,
  author = {Xie, Shaowen and Zhu, Hao and Liu, Zhen and Zhang, Qi and Zhou, You and Cao, Xun and Ma, Zhan},
  title = {DINER: Disorder-Invariant Implicit Neural Representation},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1--10},
  year = {2023}
}