Determination of the optimal regularization parameters in hyperspectral tomography

Area of Science:

• Spectroscopy
• Optical Imaging
• Chemical Sensing

Background:

• Hyperspectral absorption spectroscopy enables simultaneous tomographic imaging of temperature and species concentration.
• Previous work established a nonlinear minimization approach for tomographic inversion.

Purpose of the Study:

• To develop an effective method for determining optimal regularization parameters in nonlinear optimization for hyperspectral tomography.
• To enhance the robustness of the hyperspectral tomography algorithm.

Main Methods:

• A novel, simple, and effective method for optimal regularization parameter determination.
• Integration of this method with a previously described nonlinear minimization technique.

Main Results:

• A robust algorithm for hyperspectral tomography was successfully developed.
• The method leverages inherent absorption features for improved performance.

Conclusions:

• The developed method significantly enhances hyperspectral tomography.
• This approach is adaptable to other absorption spectroscopy-based sensing techniques.