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

ABSTRACT: The energy resolution in hyperspectral imaging techniques has always been an important matter in data interpretation. In many cases, spectral information is distorted by elements such as instruments' broad optical transfer function, and electronic high frequency noises. In the past decades, advances in artificial intelligence methods have provided robust tools to better study sophisticated system artifacts in spectral data and take steps towards removing these artifacts from the experimentally obtained data. This study evaluates the capability of a recently developed deep convolutional neural network script, EELSpecNet, in restoring the reality of a spectral data. The particular strength of the deep neural networks is to remove multiple instrumental artifacts such as random energy jitters

SUBMITTER: Mousavi M SS 

PROVIDER: S-EPMC9581942 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature