Noise Removal of FTIR Hyperspectral Images via MMSE

TL;DR

This paper proposes an MMSE-based noise removal method for FTIR hyperspectral images that balances speed and accuracy, improving processing efficiency in applications like chemical warfare agent detection.

Contribution

It introduces an efficient MMSE approach for noise removal in FTIR hyperspectral images, reducing processing time while maintaining performance.

Findings

MMSE estimator achieves comparable noise removal performance to existing methods.

The proposed method reduces processing time for FTIR hyperspectral images.

Effective in applications requiring rapid and accurate spectral analysis.

Abstract

Fourier transform infrared (FTIR) hyperspectral imaging systems are deployed in various fields where spectral information is exploited. Chemical warfare agent (CWA) detection is one of such fields and it requires a fast and accurate process from the measurement to the visualization of detection results, including noise removal. A general concern of existing noise removal algorithms is a trade-off between time and performance. This paper suggests a minimum mean square error (MMSE) approach as an efficient noise removal algorithm for FTIR hyperspectral images. The experimental result shows that the MMSE estimator spends less time to achieve comparable performance to the existing algorithms.