Presentation
28 September 2023 Learning-based lensless fiber bundle imaging with real-time resolution enhancement for biomedicine
Author Affiliations +
Abstract
An end-to-end tumor diagnosis framework including resolution enhancement and tumor classification is proposed. The U-Net + EDSR network enables a significant improvement of PSNR and enhances the resolution beyond physical limitations. Moreover, the subsequent tumor discrimination can benefit from the enhancement. Multi-image as network input and advanced models like generative adversarial networks are expected to bring a further improvement for the imaging. Our proposed novel method first time realizes intraoperative lensless CFB imaging with high resolution in the near-field. The technique builds a bridge to many techniques like optical biopsies, multi-modal imaging, virtual staining, and computer-assisted disease diagnostics for neuron signal monitoring as well as neurosurgery.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tijue Wang, Jakob Dremel, Jiachen Wu, Ortrud Uckermann, Roberta Galli, Ilker Eyüpoglu, Jürgen Czarske, and Robert Kuschmierz "Learning-based lensless fiber bundle imaging with real-time resolution enhancement for biomedicine", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265501 (28 September 2023); https://doi.org/10.1117/12.2675989
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KEYWORDS
Biomedical optics

Real time imaging

Resolution enhancement technologies

Image resolution

Image enhancement

Medical image reconstruction

Endoscopy

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