Simultaneous execution of color adjustment and image completion by generative adversarial network

Naofumi Akimoto, Masaki Hayashi, Shuichi Akizuki, Yoshimitsu Aoki

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we address the problem of performing natural paste synthesis by color adjustment and image completion, in order to solve the completion problem that can specify an object appearing in a completion area. We propose a synthesis network that can extract the context features of the input image and reconstruct an image with the feature, making the inserted object appear in the completion region. In addition, we propose a ingenious method to make input images and learning method using Generative Adversarial Network (GAN) that do not require collection of high cost learning data. We show that color adjustment and image completion based on context features are executed at the same time, and natural pasting synthesis can be performed by using these proposal methods.

Original languageEnglish
Pages (from-to)1033-1040
Number of pages8
JournalSeimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
Volume84
Issue number12
DOIs
Publication statusPublished - 2018

Keywords

  • Color adjustment
  • Computer graphics
  • Computer vision
  • Convolutional neural network
  • Generative adversarial nets
  • Image completion
  • Inpainting
  • Machine learning

ASJC Scopus subject areas

  • Mechanical Engineering

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