1. National Engineering Research Center of Visual Technology, School of Computer Science, Peking University
2. School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School
3. School of Information and Communication Engineering, UESTC
This repository contains the official source code for our paper:
Spike Camera Image Reconstruction Using Deep Spiking Neural Networks
TCSVT 2023
You can choose cudatoolkit version to match your server. The code is tested on PyTorch 2.0.1+cuda12.0.
conda create -n ssir python==3.10
conda activate ssir
# You can choose the PyTorch version you like, we recommand version >= 1.10.1
# For example
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
pip install -r requirements.txt
BaiduNetDisk (Password: 2728)
train.tar
corresponds to the training data, and test.tar
corresponds to the testing data.
Move the above two .tar
file to the data root
directory and extract to the current directory
file directory:
train:
your_data_root/crop_mini/spike/...
your_data_root/crop_mini/image/...
test:
your_data_root/spike/...
your_data_root/imgs/...
In the line25 of main.py
or set that in command line when running main.py
cd shells
bash eval_SREDS.sh
cd shells
bash train_SSIR.sh
We recommended to redirect the output logs by adding
>> SSIR.txt 2>&1
to the last of the above command for management.
If you find this code useful in your research, please consider citing our paper.
@article{zhao2023spike,
title={Spike Camera Image Reconstruction Using Deep Spiking Neural Networks},
author={Zhao, Rui and Xiong, Ruiqin and Zhang, Jian and Yu, Zhaofei and Zhu, Shuyuan and Ma, Lei and Huang, Tiejun},
journal={IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)},
year={2023},
}
If you have any questions, please contact:
[email protected]