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Introduction

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Fig1. The Block Diagram of Video Snapshot Compressive Imaging

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Video Snapshot Compressive Imaging (Video SCI) mainly focus on capturing highspeed scenario using a low speed camera and capturing high dimensional data in a single shot in order to minimize network transmission bandwidth, lowering time and hardware cost. These goals can be achieved by capturing 3D video data using a 2D detector and then storing and transmitting the video data into a 2D data matrix.

CACTI (Coded Aperture Compressive Temporal Imaging) is an classical Video SCI system. This library is implemented based on CACTI reconstruction algorithm via PyTorch. This library basically include all mainstream Video SCI algorithm.

CACTI Library was born in SCI Lab, Westlake University. We sincerely hope our library users can make their own contributions in various Video SCI algorithms, and by implementing and referring this library, one can broaden the library users and share deep understanding about Video SCI.

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Fig2. Reconstructed Gray Value Data via Different Algorithms

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Fig3. Reconstructed Colored Data via Different Algorithms

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Supported Algorithms

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Fig3. Reconstruction Quality Comparison between Different Algorithms on Testing Dataset

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Iterative Optimization Method

End to End Algorithm

Deep Unfolding Algorithm

Plug and Play Algorithm

All model parameters can be download at Dropbox and BaiduNetdisk

CACTI Installation

Please see the Installation Manual for CACTI Installation

CACTI Code Library

Acknowledgement

Many appreciate to all the contributors who share their work for this library. Video SCI library always open and seeking for more Video SCI algorithms.

Citation

@article{2021Snapshot,
  title={Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications},
  author={ Yuan, X.  and  Brady, D. J.  and  Katsaggelos, A. K. },
  journal={IEEE Signal Processing Magazine},
  year={2021},
}