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.
Highlights
Push-broom line-scanning hyperspectral cameras can be used for a wide range of applications, e.g., remote sensing, food quality control and waste sorting [1,2,3,4]
The maximum noise fraction transform [10] is commonly used for noise removal in hyperspectral imaging
The algorithms presented in our study represent the minimum noise fraction (MNF)
Summary
Push-broom line-scanning hyperspectral cameras can be used for a wide range of applications, e.g., remote sensing, food quality control and waste sorting [1,2,3,4]. The transform is able to efficiently separate signal and noise by reordering the signal space according to increasing SNR (signal-to-noise ratio) This can be used to remove noisy components, while retaining both spectral and spatial resolution. The algorithms presented in our study represent the minimum noise fraction (MNF)
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