EfficientSCI

This repo is the implementation of EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging.

Testing Result on Simulation Dataset

Fig1. Comparison of reconstruction quality and testing time of several SOTA deep learning based algorithms.

Installation

Please see the Installation Manual for EfficientSCI Installation.

Training

Support multi GPUs and single GPU training efficiently. First download DAVIS 2017 dataset from DAVIS website, then modify data_root value in configs/_base_/davis.py file, make sure data_root link to your training dataset path.

Launch multi GPU training by the statement below:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4  --master_port=3278 tools/train.py configs/EfficientSCI/efficientsci_base.py --distributed=True

Launch single GPU training by the statement below.

Default using GPU 0. One can also choosing GPUs by specify CUDA_VISIBLE_DEVICES

python tools/train.py configs/EfficientSCI/efficientsci_base.py

Testing EfficientSCI on Grayscale Simulation Dataset

Specify the path of weight parameters, then launch 6 benchmark test in grayscale simulation dataset by executing the statement below.

python tools/test.py configs/EfficientSCI/efficientsci_base.py --weights=checkpoints/efficientsci_base.pth

Testing EfficientSCI in Color Simulation Dataset

First, download the model weight file (checkpoints/efficientsci/efficientsci_base_mid_color.pth) and test data (datasets/middle_scale) from Dropbox or BaiduNetdisk, and place them in the checkpoints folder and test_datasets folder respectively. Then, execute the statement below to launch EfficientSCI in 6 middle color simulation dataset.

python tools/test.py configs/EfficientSCI/efficientsci_base_mid_color.py --weights=checkpoints/efficientsci_base_mid_color.pth

Citation

@inproceedings{wang2023efficientsci,
  title={Efficientsci: Densely connected network with space-time factorization for large-scale video snapshot compressive imaging},
  author={Wang, Lishun and Cao, Miao and Yuan, Xin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={18477--18486},
  year={2023}
}