A novel reconstruction method for compressive spectral imaging is designed by assuming that the spectral image of interest is sufficiently smooth on a collection of graphs. Since the graphs are not known in advance, we propose to infer them from a panchromatic image using a state-of-the-art graph learning method. Our approach leads to solutions with closed-form that can be found efficiently by solving multiple sparse systems of linear equations in parallel. Extensive simulations and an experimental demonstration show the merits of our method in comparison with traditional methods based on sparsity and total variation and more recent methods based on low-rank minimization and deep-based plug-and-play priors. Our approach may be instrumental in designing efficient methods based on deep neural networks and covariance estimation.
|Number of pages||23|
|State||Published - Feb 28 2022|
Bibliographical noteFunding Information:
National Science Foundation (1815992, 1816003).
Acknowledgments. Portions of this work were presented at the OSA Imaging and Applied Optics Congress in 2021, Compressive Spectral Imaging using Smoothness on Graphs . Juan F. Florez thanks Fulbright Colombia and Colciencias for his doctoral fellowship, the University of Delaware’s graduate college for his dissertation fellowship, and Hoover Rueda, Carlos Mendoza, and Wenyi Ren for insightful and helpful technical discussions. This material is based upon work supported by the National Science Foundation under Grants NSF 1815992 and NSF 1816003.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics