抄録
We address the problem of automatic photo enhancement, in which the challenge is to determine the optimal enhancement for a given photo according to its content. For this purpose, we train a convolutional neural network to predict the best enhancement for given picture. While such machine learning techniques have shown great promise in photo enhancement, there are some limitations. One is the problem of interpretability, i.e., that it is not easy for the user to discern what has been done by a machine. In this work, we leverage existing manual photo enhancement tools as a black-box model, and predict the enhancement parameters of that model. Because the tools are designed for human use, the resulting parameters can be interpreted by their users. Another problem is the difficulty of obtaining training data.We propose generating supervised training data from high-quality professional images by randomly sampling realistic de-enhancement parameters. We show that this approach allows automatic enhancement of photographs without the need for large manually labelled supervised training datasets.
本文言語 | English |
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ホスト出版物のタイトル | SIGGRAPH Asia 2018 Technical Briefs, SA 2018 |
出版社 | Association for Computing Machinery, Inc |
ISBN(電子版) | 9781450360623 |
DOI | |
出版ステータス | Published - 2018 12月 4 |
イベント | SIGGRAPH Asia 2018 Technical Briefs - International Conference on Computer Graphics and Interactive Techniques, SA 2018 - Tokyo, Japan 継続期間: 2018 12月 4 → 2018 12月 7 |
Other
Other | SIGGRAPH Asia 2018 Technical Briefs - International Conference on Computer Graphics and Interactive Techniques, SA 2018 |
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国/地域 | Japan |
City | Tokyo |
Period | 18/12/4 → 18/12/7 |
ASJC Scopus subject areas
- コンピュータ グラフィックスおよびコンピュータ支援設計
- コンピュータ ビジョンおよびパターン認識
- 人間とコンピュータの相互作用
- ソフトウェア