修补
人工智能
计算机科学
发电机(电路理论)
图像(数学)
卷积神经网络
计算机视觉
失真(音乐)
模式识别(心理学)
图像复原
深度学习
纹理合成
图像质量
图像纹理
图像处理
物理
量子力学
计算机网络
功率(物理)
放大器
带宽(计算)
作者
Yuantao Chen,Runlong Xia,Ke Zou,Kai Yang
标识
DOI:10.1007/s13042-023-01811-y
摘要
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.
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