This paper proposes using both spatial and spectral regularizers/priors for hyperspectral image sharpening. Leveraging the recent plug-and-play framework, we plug two Gaussian-mixture-based denoisers into the iterations of an alternating direction method of multipliers (ADMM): a spatial regularizer learned from the observed multispectral image, and a spectral regularizer trained using the hyperspectral data. The proposed approach achieves very competitive results, improving the performance over using a single regularizer. Furthermore, the spectral regularizer can be used to classify the image pixels, opening the door to class-adapted models.
CITATION STYLE
Teodoro, A. M., Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2018). Sharpening hyperspectral images using spatial and spectral priors in a plug-and-play algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10746 LNCS, pp. 358–371). Springer Verlag. https://doi.org/10.1007/978-3-319-78199-0_24
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