Snapshot Multispectral Image Completion and Unmixing with Total Variation Regularization on Abundance Maps

Unmixing is an important application of spectral imaging, and snapshot sensors could enrich its applicability. How-ever, their spatio-spectral tradeoff decreases spatial resolution as the number of bands increases. While basis spectra can be estimated even on the downsampled multispectral image, it...

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Bibliographic Details
Published in2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) pp. 1367 - 1374
Main Authors Ozawa, Keisuke, Sumiyoshi, Shinichi, Tachioka, Yuki
Format Conference Proceeding
LanguageEnglish
Published APSIPA 14.12.2021
Subjects
Image restoration
Image sensors
Information processing
Optimization
Sensors
Spatial resolution
Tensors
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Summary:Unmixing is an important application of spectral imaging, and snapshot sensors could enrich its applicability. How-ever, their spatio-spectral tradeoff decreases spatial resolution as the number of bands increases. While basis spectra can be estimated even on the downsampled multispectral image, it is difficult to retain high-resolution abundance maps. In this paper, we propose a high spatial resolution unmixing method from a single snapshot multispectral image. The proposed method simultaneously completes a snapshot data to restore the full sensor size multispectral image. In a simulation, we show a resolution-enhanced unmixing and better completion accuracy compared with state-of-the-art tensor completion methods. We also demonstrate against real data the best quality for completion and unmixing in the full sensor size.
ISSN:2640-0103