Optimization-based data generation for photo enhancement

Mayu Omiya, Yusuke Horiuchi, Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

The preparation of large amounts of high-quality training data has always been the bottleneck for the performance of supervised learning methods. It is especially time-consuming for complicated tasks such as photo enhancement. A recent approach to ease data annotation creates realistic training data automatically with optimization. In this paper, we improve upon this approach by learning image-similarity which, in combination with a Covariance Matrix Adaptation optimization method, allows us to create higher quality training data for enhancing photos. We evaluate our approach on challenging real world photo-enhancement images by conducting a perceptual user study, which shows that its performance compares favorably with existing approaches.

本文言語English
ホスト出版物のタイトルProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
出版社IEEE Computer Society
ページ1890-1898
ページ数9
ISBN(電子版)9781728125060
DOI
出版ステータスPublished - 2019 6
イベント32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
継続期間: 2019 6 162019 6 20

出版物シリーズ

名前IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2019-June
ISSN(印刷版)2160-7508
ISSN(電子版)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
国/地域United States
CityLong Beach
Period19/6/1619/6/20

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

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