Fast generative adversarial networks model for masked image restoration. Issue 7 (25th April 2019)

• Record Type: Journal Article
• Authors: Cao, Zhiyi; Niu, Shaozhang; Zhang, Jiwei; Wang, Xinyi
• Is Part Of: IET image processing. Volume 13:Issue 7(2019)
• Page Start: 1124
• Page End: 1129
• Publication Date: 2019-04-25
• Subjects: image restoration -- gallium compounds -- wide band gap semiconductors -- III‐V semiconductors; neighbouring network -- fast generative adversarial networks model -- complex masked region -- masked image -- conventional masked image restoration algorithms -- fast marching method -- discriminator network -- parsing networks -- GaN; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5592
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.