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Image Completion Based on Edge Prediction and Improved Generator

机译：基于边缘预测和改进发电机的图像完成

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摘要

The existing image completion algorithms may result in problems of poor completion in the missing parts, excessive smoothing or chaotic structure of the completed areas, and large training cycle when processing more complex images. Therefore, a two-stage adversarial image completion model based on edge prediction and improved generator structure has been put forward to solve the existing problems. Firstly, Canny edge detection is utilized to extract the damaged edge image, to predict and to complete the edge information of the missing area of the image in the edge prediction network. Secondly, the predicted edge image is taken as a priori information by the Image completion network to complete the damaged area of the image, so as to make the structure information of the completed area more accurate. A-JPU module is designed to ensure the completion result and speed up training for existing models due to the enormous number of computations caused by the large use of extended convolution in the self-coding structure. Finally, the experimental results on Places 2 dataset show that the PSNR and SSIM of the image using the image completion model are higher and the subjective visual effect is closer to the real image than some other image completion models.
机译：现有的图像完成算法可能导致在缺失部件，过度平滑或完成区域的混沌结构中完成的问题，以及在处理更复杂的图像时的大训练周期。因此，已经提出了一种基于边缘预测和改进的发电机结构的两阶段对抗性图像完成模型来解决现有问题。首先，利用Canny边缘检测来提取损坏的边缘图像，以预测和完成边缘预测网络中图像的缺失区域的边缘信息。其次，通过图像完成网络被视为先验的边缘图像以完成图像的损坏区域，以便使完成区域的结构信息更准确。 A-JPU模块旨在确保由于在自编码结构中大量使用延长卷积而导致的巨大计算，确保完成结果和加速现有模型的培训。最后，在第2个数据集上的实验结果表明，使用图像完成模型的图像的PSNR和SSSIM更高，并且主观视觉效果比其他一些图像完成模型更接近真实图像。

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