This repository contains the official source code for our paper:

Spike Camera Image Reconstruction Using Deep Spiking Neural Networks

TCSVT 2023

Paper

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
CUDA_VISIBLE_DEVICES=[] bash eval_SREDS.sh

cd shells
CUDA_VISIBLE_DEVICES=[] 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:
ruizhao@stu.pku.edu.cn