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

被引：1

作者：

Kim, Min Beom ^{[1
]}

Lee, Sanglyn ^{[1
]}

Kim, Ilho ^{[1
]}

Hong, Hee Jung ^{[1
]}

Kim, Chang Gone ^{[1
]}

Yoon, Soo Young ^{[1
]}

机构：

[1] LG Display R&D Ctr, LG Sci Pk, Seoul 6853, South Korea

来源：

JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY | 2020年 / 28卷 / 05期

关键词：

deep learning; super-resolution;

D O I：

10.1002/jsid.902

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：428 / 439

页数：12

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