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access icon free Fast generative adversarial networks model for masked image restoration

© The Institution of Engineering and Technology
Received 30/05/2018, Accepted 05/02/2019, Published 11/03/2019

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.

Inspec keywords: wide band gap semiconductors; image restoration; III-V semiconductors; gallium compounds

Other keywords: GaN; masked image; conventional masked image restoration algorithms; fast generative adversarial networks model; neighbouring network; discriminator network; complex masked region; parsing networks; fast marching method

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

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