Abstract
Though deep learning based spectral super-resolution (SSR) methods have state-of-the-art performances, most previous deep spectral super-resolution approaches require extensive paired RGB images and hyperspectral images (HSIs) for well-fitting learning. However, in real cases, the cost of generating such paired images is too prohibitive to collect sufficient training samples. To solve this problem, we investigated one-shot SSR in a target domain. To avoid over-fitting, we introduced knowledge from a source domain to guide the one-shot SSR in the target domain and use the idea of spectral unmixing to remove the interference of different spectral characteristics, with which we proposed a spectral-unmixing inspired deep SSR framework. Experimental results on three benchmark SSR datasets showed the effectiveness of the proposed method.
Original language | English |
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Article number | 9226128 |
Pages (from-to) | 1459-1470 |
Number of pages | 12 |
Journal | IEEE Transactions on Computational Imaging |
Volume | 6 |
DOIs | |
State | Published - 2020 |
Keywords
- deep neural network
- Hyperspectral imagery
- one-shot learning
- spectral super-resolution
- transfer learning