Deep-learning-based hyperspectral recovery from a single RGB image

Junchao Zhang, Yuanyuan Sun, Jianlai Chen, Degui Yang, Rongguang Liang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)5676-5679
Number of pages4
JournalOptics letters
Volume45
Issue number20
DOIs
StatePublished - Oct 15 2020

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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