Snapshot Compressive Imaging: Theory, Algorithms, and Applications

By Xin Yuan, David J. Brady, Aggelos K. Katsaggelos. IEEE Signal Processing Magazine, 2021

332 citations recorded by the source corpus.

Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥3D) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the HD data in a compressive manner; following this, algorithms are employed to reconstruct the desired HD data cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, and so on. Although the hardware has been investigated for more than a decade, the theoretical guarantees have only recently been derived. Inspired by deep learning, various deep neural networks have also been developed to reconstruct the HD data cube in spectral SCI and video SCI. This article reviews recent advances in SCI hardware, theory, and algorithms, including both optimizationbased and deep learning-based algorithms. Diverse applications and the outlook for SCI are also discussed.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/dd49eeff238055f3fb67f53ccb6ce219024ab900

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Radu Timofte
2. Luc Van Gool
3. Hanspeter Pfister
4. Xiaowan Hu
5. Jing Lin
6. Haoqian Wang
7. Xin Yuan
8. Yulun Zhang

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Radu Timofte
3. Yulun Zhang
4. Ming-Hsuan Yang
5. Chen Change Loy
6. Lei Zhang
7. Chao Dong
8. Andrew Zisserman

In Radiology, Nuclear Medicine and Imaging

1. Luc Van Gool

Citations in the corpus

• MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (2022, background): “To address these limitations, researchers have developed SCI systems [18, 26, 50, 71, 72] to capture HSIs, where the 3D HSI cube is compressed into a single 2D measurement [81].”
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (2021, background): “These SCI systems compress information of snapshots along the spectral dimension into one single 2D measurement [58].”