多尺度生成式对抗网络图像修复算法

图像修复作为深度学习领域的一个研究热点,在人们现实生活中有着重要的意义。现有图像修复算法存在各种问题,导致视觉上无法达到人们的要求。针对现有图像修复算法精确度低、视觉一致性差以及训练不稳定等缺陷,提出了一种基于生成式对抗网络(GAN)模型的图像修复算法。该算法主要对判别器的网络结构进行改进,在全局判别器和局部判别器的基础上引入多尺度判别器。多尺度判别器在不同分辨率的图像上进行训练,不同尺度的判别器具有不同的感受野,能够引导生成器生成更全局的图像视图以及更精细的细节。针对GAN训练中经常出现的梯度消失或梯度爆炸问题,使用WGAN(Wasserstein GAN)的思想,采用EM距离模拟样本数据分布。在CelebA、ImageNet以及Place2图像数据集上对该算法的网络模型进行了训练和测试,结果显示与先前的算法模型相比,该算法提高了图像修复的精确度,能够生成更为逼真的修复图片,并且适用于多种类型图片的修复。

Multi-Scale Generative Adversarial Networks Image Inpainting Algorithm

As a research hotspot in the field of deep learning, image inpainting has important significance in peoples real life. Existing image inpainting methods have various problems that cause visual failure to meet peoples requirements.Aiming at the defects of low accuracy, poor visual consistency and unstable training, this paper proposes an image inpainting algorithm based on the generative adversarial network(GAN) model. The algorithm mainly improves the network structure of the discriminator, and introduces a multi-scale discriminator based on the global discriminator and the local discriminator. The multi-scale discriminators are trained on images of different resolutions. Different scale discriminators have different receptive fields, and guide the generator to generate a more global image view and finer details. Aiming at the problem of gradient disappearance or gradient explosion that often occurs in GAN training, the idea of Wasserstein GAN(WGAN) is adopted, and the EM distance is used to simulate the sample data distribution. The network model of the algorithm is trained and tested on the CelebA, ImageNet and Place2 image datasets. The results show that compared with the previous algorithm models, this algorithm improves the accuracy of image inpainting, can generate more realistic inpainting images, and is suitable for many types of image inpainting.