Abstract
In this chapter, we present a low dimensional manifold model (LDMM) for hyperspectral image reconstruction. This model is based on the observation that the spatial–spectral blocks of hyperspectral images typically lie close to a collection of low dimensional manifolds. To emphasize this, we directly use the dimension of the manifold as a regularization term in a variational functional, which can be solved efficiently by alternating direction of minimization and advanced numerical discretization. Experiments on the reconstruction of hyperspectral images from sparse and noisy sampling demonstrate the superiority of LDMM in terms of both speed and accuracy.
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Acknowledgements
This work was supported by STROBE: A National Science Foundation Science & Technology Center, under Grant No. DMR 1548924 as well as DOE-DE-SC0013838 and NSFC 11671005.
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Zhu, W., Shi, Z., Osher, S. (2020). Low Dimensional Manifold Model in Hyperspectral Image Reconstruction. In: Prasad, S., Chanussot, J. (eds) Hyperspectral Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-38617-7_10
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DOI: https://doi.org/10.1007/978-3-030-38617-7_10
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