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Unsupervised learning for hyperspectral recovery based on a single RGB image

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Abstract

Hyperspectral imagery often suffers from the degradation of spatial, spectral, or temporal resolution due to the limitations of hyperspectral imaging devices. To address this problem, hyperspectral recovery from a single red-green-blue (RGB) image has recently achieved significant progress via deep learning. However, current deep learning-based methods are all learned in a supervised way under the availability of RGB and correspondingly hyperspectral images, which is unrealistic for practical applications. Hence, we propose to recover hyperspectral images from a single RGB image in an unsupervised way. Moreover, based on the statistical property of hyperspectral images, a customized loss function is proposed to boost the performance. Extensive experiments on the BGU iCVL Hyperspectral Image Dataset demonstrate the effectiveness of the proposed method.

© 2021 Optical Society of America

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Data Availability

Data underlying the results presented in this paper are available in Ref. [12].

12. B. Arad and O. Benshahar, in European Conference on Computer Vision (2016), pp. 19–34.

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