Hyperspectral phase retrieval: spectral–spatial data processing with sparsity-based complex domain cube filter

Research output: Contribution to journalArticleScientificpeer-review

5 Downloads (Pure)

Abstract

Hyperspectral (HS) imaging retrieves information from data obtained across broadband spectral channels. Information to retrieve is a 3D cube, where two coordinates are spatial and the third one is spectral. This cube is complex-valued with varying amplitude and phase. We consider shearography optical setup, in which two phase-shifted broadband copies of the object projections are interfering at a sensor. Registered observations are intensities summarized over spectral channels. For phase reconstruction, the variational setting of the phase retrieval problem is used to derive the iterative algorithm, which includes the original proximity spectral analysis operator and the sparsity modeling of the complex-valued object 3D cube. We resolve the HS phase retrieval problem without random phase coding of wavefronts typical for the most conventional phase retrieval techniques. We show the performance of the algorithm for object phase and thickness imaging in simulation and experimental tests.
Original languageEnglish
Article number013108
JournalOptical Engineering
Volume60
Issue number1
DOIs
Publication statusPublished - Jan 2021
Publication typeA1 Journal article-refereed

Publication forum classification

  • Publication forum level 1

Fingerprint

Dive into the research topics of 'Hyperspectral phase retrieval: spectral–spatial data processing with sparsity-based complex domain cube filter'. Together they form a unique fingerprint.

Cite this