Efficient multiquality super‐resolution using a deep convolutional neural network for an FPGA implementation | Zendy

Kim Min Beom | Zendy; Lee Sanglyn | Zendy; Kim Ilho | Zendy; Hong Hee Jung | Zendy; Kim Chang Gone | Zendy; Yoon Soo Young | Zendy

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Efficient multiquality super‐resolution using a deep convolutional neural network for an FPGA implementation

Author(s) -

Kim Min Beom,

Lee Sanglyn,

Kim Ilho,

Hong Hee Jung,

Kim Chang Gone,

Yoon Soo Young

Publication year - 2020

Publication title -

journal of the society for information display

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.578

H-Index - 52

eISSN - 1938-3657

pISSN - 1071-0922

DOI - 10.1002/jsid.902

Subject(s) - computer science , field programmable gate array , convolutional neural network , trimming , application specific integrated circuit , artificial neural network , quality (philosophy) , resolution (logic) , artificial intelligence , embedded system , computer architecture , computer hardware , computer engineering , real time computing , operating system , philosophy , epistemology

We propose an efficient deep convolutional neural network for a super‐resolution which is capable of multiple‐quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.

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