MRGAN: a generative adversarial networks model for global mosaic removal

被引：4

作者：

Cao, Zhiyi ^{[1
]}

Niu, Shaozhang ^{[1
]}

Zhang, Jiwei ^{[1
]}

Wang, Xinyi ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing, Peoples R China

来源：

IET IMAGE PROCESSING | 2020年 / 14卷 / 10期

关键词：

image segmentation; image resolution; neural nets; local mosaic removal; MRGAN model; global mosaic removal task; deep generative adversarial network model; maintaining and repairing images; parsing networks; pixel loss; content loss;

D O I：

10.1049/iet-ipr.2019.1111

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2235 / 2240

页数：6

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