Chen Yuantao, Xia Runlong, Zou Ke, Yang Kai
School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, Hunan China.
Mountain Yuelu Breeding Innovation Center Limited, Changsha, China.
Int J Mach Learn Cybern. 2023 Mar 25:1-17. doi: 10.1007/s13042-023-01811-y.
In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information. However, the existing premier convolutional neural networks methods usually causes the problems of excessive color difference and image texture loss and distortion phenomenon. The paper has proposed an effective image inpainting method using generative adversarial networks, which is composed of two mutually independent generative confrontation networks. Among them, the image repair network module aims to solve the problem of repairing the irregular missing areas of the image, and its generator is based on a partial convolutional network. The image optimization network module aims to solve the problem of local chromatic aberration in the repaired images, and its generator has based on deep residual networks. Through the synergy of the two network modules, the visual effect and image quality of the images has improved. The experimental results can show that the proposed method (RNON) performs better from comparisons of qualitative and quantitative evaluations with state-of-the-arts in image inpainting quality field.
在过去几年中，基于深度学习模型的图像修复方法与现有的传统方法相比显示出明显的优势。前者能够更好地生成视觉上合理的图像结构和纹理信息。然而，现有的主流卷积神经网络方法通常会导致色差过大以及图像纹理丢失和失真的问题。本文提出了一种使用生成对抗网络的有效图像修复方法，该方法由两个相互独立的生成对抗网络组成。其中，图像修复网络模块旨在解决修复图像中不规则缺失区域的问题，其生成器基于部分卷积网络。图像优化网络模块旨在解决修复后图像中的局部色差问题，其生成器基于深度残差网络。通过两个网络模块的协同作用，图像的视觉效果和图像质量得到了提升。实验结果表明，从与图像修复质量领域的现有最先进技术进行定性和定量评估的比较来看，所提出的方法（RNON）表现更好。
