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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.