Learning-based high-resolution lensless fiber microendoscopy (Conference Presentation)

Juergen W. Czarske; Jiachen Wu; Tijue Wang; Tom Glosemeyer; Julian Andreas Lich; Robert Kuschmierz

doi:10.1117/12.2633990

4 October 2022 Learning-based high-resolution lensless fiber microendoscopy (Conference Presentation)

Juergen W. Czarske, Jiachen Wu, Tijue Wang, Tom Glosemeyer, Julian Andreas Lich, Robert Kuschmierz

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040U (2022) https://doi.org/10.1117/12.2633990
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Minimally invasive endoscopes are indispensable in biomedicine. Coherent fiber bundles (CFB) enable lensless endoscopes. However, the aberration correction is challenging. Instead of involving bulky devices, deep neural networks (DNN) will be used. The novel approach uses speckles, which are decoded by DNN to retrieve the 3D object information. Besides this far-field approach, near-field CFB-based high-resolution imaging is promising for neurosurgery. However, the inherent honeycomb artifacts of CFB reduce the resolution. The inherent artifact is eliminated by DNN and high frequency information could be retrieved. Both methods have smart concepts in common, and both pave the way towards early recognition of diseases.

Conference Presentation

Citation Download Citation

Juergen W. Czarske, Jiachen Wu, Tijue Wang, Tom Glosemeyer, Julian Andreas Lich, and Robert Kuschmierz "Learning-based high-resolution lensless fiber microendoscopy (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040U (4 October 2022); https://doi.org/10.1117/12.2633990

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