G
Gwanggil Jeon
Researcher at Incheon National University
Publications - 620
Citations - 7038
Gwanggil Jeon is an academic researcher from Incheon National University. The author has contributed to research in topics: Computer science & Deinterlacing. The author has an hindex of 30, co-authored 511 publications receiving 4071 citations. Previous affiliations of Gwanggil Jeon include Hanyang University & Xidian University.
Papers
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Proceedings ArticleDOI
Feedback Network for Image Super-Resolution
TL;DR: Zhang et al. as mentioned in this paper proposed an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information, which uses hidden states in a recurrent neural network (RNN) with constraints to achieve such feedback manner.
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Feedback Network for Image Super-Resolution
TL;DR: Zhang et al. as discussed by the authors proposed an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information, which uses hidden states in an RNN with constraints to achieve such feedback manner.
Journal ArticleDOI
A deep learning-based social distance monitoring framework for COVID-19.
TL;DR: Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model.
Journal ArticleDOI
Deep learning in big data Analytics: A comparative study
Bilal Jan,Haleem Farman,Murad Khan,Muhammad Imran,Ihtesham Ul Islam,Awais Ahmad,Shaukat Ali,Gwanggil Jeon +7 more
TL;DR: The comparative study shows that deep learning techniques can be built by introducing a number of methods in combination with supervised and unsupervised training techniques, and indicates that high involvement of humans to design sophisticated and optimized algorithms based on machine and deeplearning techniques is high.
Journal ArticleDOI
Least-Squares Luma–Chroma Demultiplexing Algorithm for Bayer Demosaicking
TL;DR: A luma-chroma demultiplexing algorithm is presented in detail, using a least-squares design methodology for the required bandpass filters, and it was found to provide excellent performance and the best quality-speed tradeoff among the methods studied.