Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imagingopen access
- Authors
- Kim, Eunchan; Kim, Seonghoon; Choi, Myunghwan; Seo, Taewon; Yang, Sungwook
- Issue Date
- Jan-2023
- Publisher
- MDPI
- Keywords
- fiber bundle imaging; honeycomb artifact; pattern synthesis; convolution neural network (CNN)
- Citation
- SENSORS, v.23, no.1, pp.1 - 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 23
- Number
- 1
- Start Page
- 1
- End Page
- 14
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182338
- DOI
- 10.3390/s23010333
- ISSN
- 1424-8220
- Abstract
- 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.
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