| Paper ID | IVMSP-14.6 |
| Paper Title |
LAPLACIAN REGULARIZED TENSOR LOW-RANK MINIMIZATION FOR HYPERSPECTRAL SNAPSHOT COMPRESSIVE IMAGING |
| Authors |
Yi Yang, Fei Jiang, Hongtao Lu, Shanghai Jiao Tong University, China |
| Session | IVMSP-14: Hyperspectral Imaging |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVELI] Electronic Imaging |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Snapshot Compressive Imaging (SCI) systems, including hyperspectral compressive imaging and video compressive imaging, are designed to depict high-dimensional signals with limited data by mapping multiple images into one. One key module of SCI systems is a high-quality reconstruction algorithm for original frames. However, most existing decoding algorithms are based on vectorization representation and fail to capture the intrinsic structural information of high-dimensional signals. In this paper, we propose a tensor-based low-rank reconstruction algorithm with hyper-Laplacian constraint for hyperspectral SCI systems. First, we integrate the non-local self-similarity and tensor low-rank minimization approach to explore the intrinsic structural correlations along spatial and spectral domains. Then, we introduce a hyper-Laplacian constraint to model the global spectral structures, alleviating the ringing artifacts in the spatial domain. Experimental results on hyperspectral image corpus demonstrate the proposed algorithm achieves average 0.82.9 dB improvement in PSNR over state-of-the-art work. |
