计算机科学与探索2019，Vol.13Issue(8)：1402-1410,9.DOI:10.3778/j.issn.1673-9418.1811025
生成式对抗网络在图像补全中的应用*
Application of Generative Adversarial Networks in Image Completion*
摘要
Abstract
Image completion is an important research direction in the field of digital image processing and has broad application prospects. This paper proposes an image completion method based on generative adversarial networks (GAN). The generative adversarial networks model consists of two parts: the generator model and the discriminator model, all of which are implemented by convolutional neural network (CNN). Firstly, the missing region of the image is complemented by the generator model. Then, the complemented image is discriminated by the discriminator model. In order to enhance the processing power of image texture details, Markov random field (MRF) and mean square error (MSE) are used as the loss function to train the generator model. The experimental results show that the image completion method based on the generative adversarial networks has better completion effect than other existing methods.关键词
图像补全/生成式对抗网络/卷积神经网络/马尔科夫随机场/均方误差Key words
image completion/generative adversarial networks (GAN)/convolutional neural network (CNN)/Markov random field (MRF)/mean square error (MSE)分类
信息技术与安全科学引用本文复制引用
时澄,潘斌,郭小明,李芹芹,张露月,钟凡..生成式对抗网络在图像补全中的应用*[J].计算机科学与探索,2019,13(8):1402-1410,9.基金项目
The National Natural Science Foundation of China under Grant Nos. 61602228, 61572290 (国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Liaoning Province under Grant No. 2015020041 (辽宁省自然科学基金) （辽宁省自然科学基金）
the Revitalization Talents Program of Liaoning Province under Grant No. XLYC1807266 (辽宁省2018"兴辽英才计划"青年拔尖人才支持计划). （辽宁省2018"兴辽英才计划"青年拔尖人才支持计划）
