Superpixels meet essential spectra for fast Raman hyperspectral microimaging.

Area of Science:

• Spectroscopy
• Data Analysis
• Microscopy

Background:

• Spectral unmixing is crucial for analyzing hyperspectral data, requiring identification of linearly independent spectral signatures.
• Fast acquisition of hyperspectral images is essential for real-time applications like Raman microimaging.
• Existing methods can be time-consuming, limiting their applicability.

Purpose of the Study:

• To accelerate spectral unmixing in Raman hyperspectral microimaging.
• To leverage spatial priors (superpixels) for enhanced data acquisition speed.
• To assess the trade-off between acquisition speed and spectral accuracy.

Main Methods:

• Utilized Fourier domain decomposition for on-the-fly extraction of essential spectral information.
• Integrated superpixel segmentation, informed by spatial priors, to reduce redundant spectral measurements.
• Applied the combined approach to simulated and real Raman hyperspectral microimaging data.

Main Results:

• Achieved an approximate three-order-of-magnitude reduction in data acquisition time.
• Demonstrated minimal distortions in the estimated spectra of image components.
• Validated the method's effectiveness on both simulated and experimental datasets.

Conclusions:

• The proposed method significantly accelerates Raman hyperspectral microimaging via efficient spectral unmixing.
• Superpixel integration, combined with Fourier domain analysis, offers a powerful strategy for rapid hyperspectral data acquisition.
• This technique holds promise for real-time analysis in various scientific and industrial applications.