Open AccessFast generative adversarial networks model for masked image restoration
Author(s) -
Cao Zhiyi,
Niu Shaozhang,
Zhang Jiwei,
Wang Xinyi
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5592
Subject(s) - discriminator , computer science , image (mathematics) , artificial intelligence , parsing , generator (circuit theory) , image restoration , generative grammar , pattern recognition (psychology) , adversarial system , pixel , generative model , computer vision , image processing , detector , telecommunications , power (physics) , physics , quantum mechanics
The conventional masked image restoration algorithms all utilise the correlation between the masked region and its neighbouring pixels, which does not work well for the larger masked image. The latest research utilises Generative Adversarial Networks (GANs) model to generate a better result for the larger masked image but does not work well for the complex masked region. To get a better result for the complex masked region, the authors propose a novel fast GANs model for masked image restoration. The method used in authors’ research is based on GANs model and fast marching method (FMM). The authors trained an FMMGAN model which consists of a neighbouring network, a generator network, a discriminator network, and two parsing networks. A large number of experimental results on two open datasets show that the proposed model performs well for masked image restoration.