Capsule Networkを用いた顔画像の超解像手法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/11/01

タイトル(英語): Super-Resolution Method of Face Image using Capsule Network

著者名: 引地　郁海（関東学院大学 理工学部），原　翔悟（関東学院大学 理工学部），元木　誠（関東学院大学 理工学部）

著者名(英語): Ikumi Hikichi (Department of Science and Engineering, Kanto Gakuin University), Syogo Hara (Department of Science and Engineering, Kanto Gakuin University), Makoto Motoki (Department of Science and Engineering, Kanto Gakuin University)

キーワード: 超解像，カプセルネットワーク，ディープラーニング　　super-resolution，capsule network，deep learning

要約(英語): In recent years, super-resolution using deep learning has attracted attention. Super-resolution is a technology for converting low-quality images to high-quality images. Super-resolution can be applied to the technology to identify a criminal from the video of a security camera. It is difficult to identify the criminal from raw images, because security cameras are low image quality to record long-term images. In this study, we propose a method to super-resolution human face images using Capsule network. Capsule Network represents input values and output values as vectors, which makes it possible to learn features between a positional relationship and an orientation of faces. Therefore, it can be expected to generate a face image of higher quality than Convolutional Neural Network (CNN). We employ the CelebA data set,which is collected about 200,000 face images, as the training data. The low quality image is generated from the original CelebA image. Capsule network is trained using original high quality images as outputs and low quality images as inputs. Experimental results show that the super-resolution method using capsule network generates high quality face image than CNN.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.11 （2020） 特集：電気関係学会関西連合大会

本誌掲載ページ: 1270-1277 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/11/140_1270/_article/-char/ja/