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1887

access icon free Fast HSI super resolution using linear regression

© The Institution of Engineering and Technology
Received 09/05/2018, Accepted 08/05/2019, Revised 09/02/2019, Published 09/05/2019

Hyperspectral imaging has great achievements in agriculture, astronomy, surveillance, and so on. However, the inherent low spatial resolution of hyperspectral imaging, unfortunately, limits its more widespread applications. Recently, hyperspectral image (HSI) super resolution addresses this problem by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), but most of these methods did not consider real-time restoration of high spatial resolution HSI. In this study, the authors propose a fast HSI super-resolution method which fills this blank. Specifically, they model the hyperspectral super resolution as a linear regression problem according to the fact that the imaging process is a linear transform and the inverse of this transform can be approximately estimated, as the spectra of a typical scene lie in a very low-dimensional space. To further exploit the low-dimensional nature of the spectra, they divide the HR-MSI and LR-HSI into several patches and learn the inverse transform patch-by-patch. Experiments on several public datasets show that their method approximates state-of-the-art methods in accuracy, but is several orders of magnitude faster than all of them. Furthermore, they provide an efficient C language implementation of their methods, which can meet the real-time request.

Inspec keywords: image reconstruction; approximation theory; image resolution; geophysical image processing; regression analysis; image fusion; hyperspectral imaging; remote sensing

Other keywords: low-dimensional space; C language implementation; imaging process; high spatial resolution multispectral image; low spatial resolution HSI; fast HSI super resolution; hyperspectral super resolution; fast HSI super-resolution method; inherent low spatial resolution; LR-HSI; hyperspectral image super resolution; HR-MSI; linear regression problem; high spatial resolution HSI

Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Numerical approximation and analysis; Computer vision and image processing techniques; Geophysics computing; Data and information; acquisition, processing, storage and dissemination in geophysics; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Geophysical techniques and equipment; Optical, image and video signal processing; Interpolation and function approximation (numerical analysis); Probability theory, stochastic processes, and statistics

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