Unsupervised learning for hyperspectral recovery based on a single RGB image

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)3977-3980
Number of pages4
JournalOptics letters
Volume46
Issue number16
DOIs
StatePublished - Aug 15 2021

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

Fingerprint

Dive into the research topics of 'Unsupervised learning for hyperspectral recovery based on a single RGB image'. Together they form a unique fingerprint.

Cite this