Learned end-to-end high-resolution lensless fiber imaging toward intraoperative real-time cancer diagnosis

Wu, Juanjuan; Wang, Tijue; Uckermann, Ortrud; Galli, Roberta; Schackert, Gabriele; Cao, Liangcai; Czarske, Jürgen; Kuschmierz, Robert

doi:10.48550/arxiv.2203.00008

Cited by 1 publication

(1 citation statement)

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“…The brightness mapping between the FB images and GT was constructed by a neural network (GARNN) [23,24] . Learned high-resolution FB images were also used for medical diagnosis, and the network helped increase the classification accuracy from 90.8% to 95.6% for glioblastoma [25] . In addition, with end-to-end deep-learning reconstruction algorithms, FB imaging systems are capable of reconstructing multispectral data with the integration of coding components [26] .…”

Section: Introductionmentioning

confidence: 99%

Feature-enhanced fiber bundle imaging based on light field acquisition

Feng,

Zhu,

2024

Advanced Imaging

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Optical fiber bundles frequently serve as crucial components in flexible miniature endoscopes, transmitting end-to-end images directly for medical and industrial applications. Each core usually acts as a single pixel, and the resolution of the image is limited by the core size and core spacing. We propose a method that exploits the hidden information embedded in the pattern within each core to break the limitation and obtain high-dimensional light field information and more features of the original image including edges, texture, and color. Intra-core patterns are mainly related to the spatial angle of captured light rays and the shape of the core. A convolutional neural network is used to accelerate the extraction of in-core features containing the light field information of the whole scene, achieve the transformation of in-core features to real details, and enhance invisible texture features and image colorization of fiber bundle images.

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