Commercial hyperspectral imaging devices are expensive and tend to suffer from the degradation of spatial, spectral, or temporal resolution. To address these problems, we propose a deep-learning-based method to recover hyperspectral images from a single RGB image. The proposed method learns an end-to-end mapping between an RGB image and corresponding hyperspectral images. Moreover, a customized loss function is proposed to boost the performance. Experimental results on a variety of hyperspectral datasets demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and perceptual quality.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics