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Sensors 2015, 15(2), 3362-3378; doi:10.3390/s150203362

Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum Noise Fraction-Based Approach

Department of Electronics and Telecommunications, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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Received: 28 August 2014 / Revised: 4 January 2015 / Accepted: 28 January 2015 / Published: 3 February 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2773 KB, uploaded 3 February 2015]   |  

Abstract

Processing line-by-line and in real-time can be convenient for some applications of line-scanning hyperspectral imaging technology. Some types of processing, like inverse modeling and spectral analysis, can be sensitive to noise. The MNF (minimum noise fraction) transform provides suitable denoising performance, but requires full image availability for the estimation of image and noise statistics. In this work, a modified algorithm is proposed. Incrementally-updated statistics enables the algorithm to denoise the image line-by-line. The denoising performance has been compared to conventional MNF and found to be equal. With a satisfying denoising performance and real-time implementation, the developed algorithm can denoise line-scanned hyperspectral images in real-time. The elimination of waiting time before denoised data are available is an important step towards real-time visualization of processed hyperspectral data. The source code can be found at http://www.github.com/ntnu-bioopt/mnf. This includes an implementation of conventional MNF denoising. View Full-Text
Keywords: MNF; hyperspectral imaging; real-time; denoising MNF; hyperspectral imaging; real-time; denoising
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bjorgan, A.; Randeberg, L.L. Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum Noise Fraction-Based Approach. Sensors 2015, 15, 3362-3378.

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