Image Completion with Discriminator Guided Context Encoder

Fatih Altay, Senem Velipasalar

Research output: Chapter in Book/Entry/PoemConference contribution

1 Scopus citations


Image completion or inpainting is a technique that is used for reconstruction of damaged or distorted regions in an image. In this paper, a new convolutional neural network model is presented for image completion. The proposed method is based on an auto-encoder and a Generative Adversarial Network structure. To succeed at this task and produce a plausible output for the damaged or distorted region(s), the auto-encoder part of the network needs to understand the content of the entire image. On the other hand, discriminators that are used in the proposed network are responsible for deciding whether or not the inpainted output has the expected quality. The general discriminator looks at the entire image to evaluate if it is consistent as a whole, while the local discriminator looks only at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained so that the discriminator networks conclude that the inpainted image is as real as the original. This approach is also aimed to reconstruct images regardless of where the damaged regions are. We compared our proposed method with two other approaches, and the results show that it performs better especially on images with low-texture.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538692189
StatePublished - Jul 2 2018
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove


  • Image completion
  • context encoder
  • discriminator
  • inpainting

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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