Alignment-invariant signal reality reconstruction in hyperspectral imaging using a deep convolutional neural network...

Area of Science:

• Spectroscopy
• Materials Science
• Data Science

Background:

• Spectral data interpretation is crucial in hyperspectral imaging but often hindered by instrumental artifacts.
• Artifacts include broad optical transfer functions and high-frequency electronic noise, distorting spectral information.
• Artificial intelligence offers advanced solutions for analyzing and correcting these sophisticated system artifacts.

Purpose of the Study:

• To evaluate the efficacy of EELSpecNet, a deep convolutional neural network, in restoring spectral data.
• To assess EELSpecNet's ability to simultaneously correct multiple instrumental artifacts.
• To compare EELSpecNet's performance against established methods for spectral data restoration.

Main Methods:

• Utilized a deep convolutional neural network (EELSpecNet) for spectral data artifact removal.
• Trained EELSpecNet on a single dataset to address various instrumental distortions.
• Applied EELSpecNet to near zero-loss electron energy loss spectroscopy (EELS) signals in Scanning Transmission Electron Microscopy (STEM).

Main Results:

• EELSpecNet successfully reduced noise and restored the original spectral data reality.
• The deep neural network effectively removed multiple artifacts, including energy jitters, signal convolution, and high-frequency noise.
• EELSpecNet demonstrated superior efficiency and robustness compared to the Bayesian statistical method, even under challenging conditions.

Conclusions:

• EELSpecNet offers a powerful and robust solution for correcting instrumental artifacts in spectral data.
• Deep learning approaches like EELSpecNet significantly advance spectral data interpretation in techniques such as EELS.
• This method shows promise for improving data quality in advanced microscopy and spectroscopy.