Computed tomography imaging spectrometry based on superiorization and guided image filtering

Abstract

Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging technique that can obtain a three-dimensional (${2D +}\lambda$) data cube of the target scene within a single exposure. Previous studies of CTIS suggest that reconstructions usually suffer from severe artifacts due to the limited number of projections available. To overcome this limitation, an iterative algorithm combining superiorization and guided image filtering is proposed to explore the intrinsic properties of the hyperspectral data cube as well as the characteristics of zero-order diffraction for the first time, to the best of our knowledge. Results from both simulative studies and proof-of-concept experiments demonstrate its superiority in suppressing artifacts and improving precision over the frequently used expectation maximization algorithm.