ArticleApplied optics2020
Depixelation and enhancement of fiber bundle images by bundle rotation.
Article in Applied optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Fiberscopic pattern removal for optimal coverage in 3D bladder reconstructions of fiberscope cystoscopy videos.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Fiber Bundle Image Reconstruction Using Convolutional Neural Networks and Bundle Rotation in Endomicroscopy.Sensors (Basel, Switzerland) · 2023Article
- Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.Sensors (Basel, Switzerland) · 2022Article
- Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Fiber bundles have become widely adopted for use in endoscopy, live-organism imaging, and other imaging applications. An inherent consequence of imaging with these bundles is the introduction of a honeycomb-like artifact that arises from the inter-fiber spacing, which obscures features of objects in the image. This artifact subsequently limits applicability and can make interpretation of the image-based data difficult. This work presents a method to reduce this artifact by on-axis rotation of the fiber bundle. Fiber bundle images were first low-pass and median filtered to improve image quality. Consecutive filtered images with rotated samples were then co-registered and averaged to generate a final, reconstructed image. The results demonstrate removal of the artifacts, in addition to increased signal contrast and signal-to-noise ratio. This approach combines digital filtering and spatial resampling to reconstruct higher-quality images, enhancing the utility of images acquired using fiber bundles.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.