Super-Resolution Hyperspectral Reconstruction With Majorization-Minimization Algorithm and Low-Rank Approximation

Hyperspectral imaging (HSI) has become an invaluable imaging tool for many applications in astrophysics or Earth observation. Unfortunately, direct observation of hyperspectral images is impossible since the actual measurements are 2-D and suffer from strong spatial and spectral degradations, especi...

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Vydáno v:IEEE transactions on computational imaging Ročník 8; s. 260 - 272
Hlavní autoři: Abi-Rizk, Ralph, Orieux, Francois, Abergel, Alain
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation
Astrophysics
Computer Science
deconvolution
Detectors
Extraterrestrial measurements
Hyperspectral imaging
Image Processing
Image reconstruction
Image resolution
Imaging
Instruments
Integral field spectroscopy
Inverse problems
Mathematical analysis
Optimization
Regularization
Spatial resolution
Spectra
Spectral bands
spectral unmixing
super resolution
Three-dimensional displays
Super Resolution
Deconvolution
Inverse Problems
Hyperspectral Imaging
Spectral unmixing
ISSN:2573-0436, 2333-9403, 2333-9403
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Popis
Shrnutí:Hyperspectral imaging (HSI) has become an invaluable imaging tool for many applications in astrophysics or Earth observation. Unfortunately, direct observation of hyperspectral images is impossible since the actual measurements are 2-D and suffer from strong spatial and spectral degradations, especially in the infrared. We present in this work an original method for high-resolution hyperspectral image reconstruction from heterogeneous 2-D measurements degraded by integral field spectroscopy (IFS) instrument. A fundamental part of this work is developing a forward model that accounts for the limitations of the IFS instrument, such as wavelength-dependent spatial and spectral blur, subsampling, and inhomogeneous sampling steps. The reconstruction method inverts the forward model using a deterministic regularization framework for edge-preserving. It fuses information from different observations and spectral bands for resolution enhancements. We rely on the Majorize-Minimize memory gradient (3MG) optimization algorithm to solve the inverse problem while considering a low-rank approximation for the unknown to handle the high-dimensionality of the problem.
Bibliografie:ObjectType-Article-1
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ISSN:2573-0436
2333-9403
2333-9403
DOI:10.1109/TCI.2022.3161849