Volume 39, Number 2, April 2021
|Page(s)||423 - 429|
|Published online||09 June 2021|
Image restoration for irregular holes based on dual discrimination generation countermeasure network
School of Information Science and Engineering, Yunnan University, Kunming 650000, China
2 Yunnan Communications Investment and Construction Group Co., Ltd, Kunming 650000, China
In order to solve the problem that the global and local generated countermeasure network cannot inpaint the random irregular large holes, and to improve the standard convolution generator, which demonstrates the defects of color difference and blur, a network architecture of inpainting irregular large holes in an image based on double discrimination generation countermeasure network is proposed. Firstly, the image generator is a U-net architecture defined by partial convolution. The normalized partial convolution only completes the end-to-end mask update for the effective pixels. The skip link in U-net propagates the context information of the image to the higher resolution, and optimizes the training results of the model with the weighted loss function of reconstruction loss, perception loss and wind grid loss. Subsequently, the adversary loss function, the dual discrimination network including the synthetic discriminator and the global discriminator are trained separately to judge the consistency between the generated image and the real image. Finally, the weighted loss functions are trained together with generating network and double discrimination network to further enhance the detail and overall consistency of the inpainted area and make the inpainted results more natural. The simulation experiment is carried out on the Place 365 standard database. The subjective and objective experimental results show that the results of the proposed method has reasonable overall and detail semantic consistency than those of the existing methods when they are used to repair random, irregular and large-area holes. The proposed method effectively overcomes the defects of blurry details, color distortion and artifacts.
Key words: image restoration / random irregular shape hole / partial convolution / synthetic discriminator / global discriminator
关键字 : 图像修复 / 随机不规则形状孔洞 / 部分卷积 / 合成判别器 / 全局判别器
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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