Chen Xiaoning, Zhao Jian
School of Electronic and Electrical Engineering, Dongguan Polytechnic, Dongguan, China.
School of Information Science and Technology, Northwest University, Xi'an, China.
Big Data. 2022 Dec;10(6):506-514. doi: 10.1089/big.2021.0203. Epub 2021 Dec 21.
With the development of generative adversarial networks (GANs), more and more researchers apply them to image inpainting technologies. However, many existing approaches caused some inpainting images to be unclear or even restore failures due to a failure to keep the consistency of the inpainted content and structures in line with the surroundings. In this article, we propose the Improved Semantic Image Inpainting Method with Deep Convolution GANs, which can resolve this inconsistency. In the proposed method, we design a patch discriminator and contextual loss to jointly perform the accuracy and effectiveness for image inpainting. In addition, we also designed a consistency loss based on deep convolutional neural networks to constrain the difference between the generated image and the original image in the feature space. Our proposed method improves the details and authenticity effectively for the inpainting images. We evaluate our proposed method on two different datasets, and the result shows that our proposed method achieves state-of-the-art results.
随着生成对抗网络（GAN）的发展，越来越多的研究人员将其应用于图像修复技术。然而，由于未能使修复内容和结构与周围环境保持一致，许多现有方法导致一些修复后的图像不清晰甚至修复失败。在本文中，我们提出了一种基于深度卷积GAN的改进语义图像修复方法，该方法可以解决这种不一致性问题。在所提出的方法中，我们设计了一个补丁判别器和上下文损失，以共同实现图像修复的准确性和有效性。此外，我们还基于深度卷积神经网络设计了一个一致性损失，以在特征空间中约束生成图像与原始图像之间的差异。我们提出的方法有效地改善了修复图像的细节和真实性。我们在两个不同的数据集上评估了我们提出的方法，结果表明我们提出的方法取得了最优的结果。
