A Fast Snapshot Hyperspectral Image Reconstruction Method Based on Three-Dimensional Low Rank Constraint
The snapshot hyperspectral imaging is an emerging technique with numerous applications. However, the hyperspectral imaging reconstruction is often time-consuming, which is placing a limit on the development of snapshot hyperspectral imaging. We present an efficient reconstruction algorithm based on...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-07-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2021.1943340 |
| Summary: | The snapshot hyperspectral imaging is an emerging technique with numerous applications. However, the hyperspectral imaging reconstruction is often time-consuming, which is placing a limit on the development of snapshot hyperspectral imaging. We present an efficient reconstruction algorithm based on the tensor analysis and the low-rank constraint. The hyperspectral data cube is regarded as a low rank three-order tensor, which can jointly treat both spatial and spectral modes. The 3D-LRC method can greatly decrease the computation time without unfolding the hyperspectral data cube into 2D patches. Compared with the-state-of-the-art method, the proposed method has a great improvement in the reconstruction speed and quality. The method has been implemented on two typical snapshot hyperspectral imaging systems. |
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| ISSN: | 1712-7971 |