Deep learning-enhanced ghost imaging through dynamic and complex scattering media with supervised corrections of dynamic scaling factors

Yang Peng, Wen Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

Ghost imaging (GI) through dynamic and complex scattering media remains challenging. The existence of dynamic scattering gives rise to a failure of GI schemes. Here, we report a deep learning-enhanced GI scheme with supervised corrections (SCGI) of dynamic scaling factors to realize high-resolution ghost reconstruction through dynamic and complex scattering media. The SCGI scheme is developed to approximate the variation of dynamic scaling factors in an optical channel and correct the recorded light intensities with a Gaussian prior. An untrained neural network powered by regularization by denoising for the SCGI scheme (SCGI-URED) is developed to further recover high-visibility ghost images. Experimental results demonstrate that high-resolution and high-visibility GI can be realized in dynamic and complex scattering media. The proposed method provides a reliable tool for implementing high-resolution and high-visibility GI through dynamic and complex scattering media and could give an impetus to developing dynamic scattering imaging in real-world scenarios.

Original languageEnglish
Article number181104
JournalApplied Physics Letters
Volume124
Issue number18
DOIs
Publication statusPublished - 29 Apr 2024

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)

Fingerprint

Dive into the research topics of 'Deep learning-enhanced ghost imaging through dynamic and complex scattering media with supervised corrections of dynamic scaling factors'. Together they form a unique fingerprint.

Cite this