{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T21:56:18Z","timestamp":1776290178017,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bio &amp; Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT)","award":["2019M3A9E2061784"],"award-info":[{"award-number":["2019M3A9E2061784"]}]},{"name":"Bio &amp; Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT)","award":["2022M3C1A3090828"],"award-info":[{"award-number":["2022M3C1A3090828"]}]},{"name":"Pioneer Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT &amp; Future Planning","award":["2019M3A9E2061784"],"award-info":[{"award-number":["2019M3A9E2061784"]}]},{"name":"Pioneer Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT &amp; Future Planning","award":["2022M3C1A3090828"],"award-info":[{"award-number":["2022M3C1A3090828"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an artifact-free image via a convolution neural network (CNN). The synthesis of honeycomb patterns on ordinary images allows conveniently learning and validating the network without the enormous ground truth collection by extra hardware setups. As a result, HAR-CNN shows significant performance improvement in honeycomb pattern removal and also detailed preservation for the 1961 USAF chart sample, compared with other conventional methods. Finally, HAR-CNN is GPU-accelerated for real-time processing and enhanced image mosaicking performance.<\/jats:p>","DOI":"10.3390\/s23010333","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T02:54:42Z","timestamp":1672282482000},"page":"333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging"],"prefix":"10.3390","volume":"23","author":[{"given":"Eunchan","family":"Kim","sequence":"first","affiliation":[{"name":"Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea"},{"name":"Department of Mechanical Convergence Engineering, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seonghoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Seoul National University, Seoul 03080, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4235-7003","authenticated-orcid":false,"given":"Myunghwan","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Seoul National University, Seoul 03080, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taewon","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Convergence Engineering, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9394-5358","authenticated-orcid":false,"given":"Sungwook","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"eaav1555","DOI":"10.1126\/sciadv.aav1555","article-title":"Optical fiber bundles: Ultra-slim light field imaging probes","volume":"5","author":"Orth","year":"2019","journal-title":"Sci. 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