Open AccessFull-spectrum denoising of high-SNR hyperspectral images
Author(s) -
Miguel Colom,
JeanMichel Morel
Publication year - 2019
Publication title -
journal of the optical society of america. a, optics, image science, and vision./journal of the optical society of america. a, online
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.803
H-Index - 158
eISSN - 1520-8532
pISSN - 1084-7529
DOI - 10.1364/josaa.36.000450
Subject(s) - hyperspectral imaging , noise reduction , redundancy (engineering) , computer science , artificial intelligence , pattern recognition (psychology) , noise (video) , video denoising , remote sensing , computer vision , image (mathematics) , geography , operating system , object (grammar) , video tracking , multiview video coding
The high spectral redundancy of hyper/ultraspectral Earth-observation satellite imaging raises three challenges: (a) to design accurate noise estimation methods, (b) to denoise images with very high signal-to-noise ratio (SNR), and (c) to secure unbiased denoising. We solve (a) by a new noise estimation, (b) by a novel Bayesian algorithm exploiting spectral redundancy and spectral clustering, and (c) by accurate measurements of the interchannel correlation after denoising. We demonstrate the effectiveness of our method on two ultraspectral Earth imagers, IASI and IASI-NG, one flying and the other in project, and sketch the major resolution gain of future instruments entailed by such unbiased denoising.