Dataset Information

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

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.

SUBMITTER: Kim E

PROVIDER: S-EPMC9824069 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

Kim Eunchan E Kim Seonghoon S Choi Myunghwan M Seo Taewon T Yang Sungwook S

Sensors (Basel, Switzerland) 20221228 1

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 re ...[more]

PMID: 36616931

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Dataset Information

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

Publications

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

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