Open AccessMRGAN: a generative adversarial networks model for global mosaic removal
Author(s) -
Cao Zhiyi,
Niu Shaozhang,
Zhang Jiwei,
Wang Xinyi
Publication year - 2020
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.2019.1111
Subject(s) - mosaic , parsing , computer science , pixel , adversarial system , generative grammar , artificial intelligence , generative adversarial network , image (mathematics) , algorithm , pattern recognition (psychology) , computer vision , archaeology , history
In this study, the authors introduce a novel deep generative adversarial networks (GANs) model for global mosaic removal. The methods used in the proposed study consist of GANs model and a novel algorithm for maintaining and repairing (MR) images. The conventional mosaic removal algorithms all employ the correlation between the inserted pixel and its neighbouring pixels, which have a limited effect on the local mosaic removal but do not work well for the global mosaic removal. To respond to this difficulty, the authors introduce an MRGAN model with two novel parsing networks. Unlike previous GANs, the MR algorithm is used to calculate the pixel loss and content loss. The experimental comparison results show that the proposed MRGAN model has achieved leading results for the global mosaic removal task.