We present a novel deep learning based algorithm for video inpainting. Video
inpainting is a process of completing corrupted or missing regions in videos.
Video inpainting has additional challenges compared to image inpainting due to
the extra temporal information as well as the need for maintaining the temporal
coherency. We propose a novel DNN-based framework called the Copy-and-Paste
Networks for video inpainting that takes advantage of additional information in
other frames of the video. The network is trained to copy corresponding
contents in reference frames and paste them to fill the holes in the target
frame. Our network also includes an alignment network that computes affine
matrices between frames for the alignment, enabling the network to take
information from more distant frames for robustness. Our method produces
visually pleasing and temporally coherent results while running faster than the
state-of-the-art optimization-based method. In addition, we extend our
framework for enhancing over/under exposed frames in videos. Using this
enhancement technique, we were able to significantly improve the lane detection
accuracy on road videos.

利用深度学习构建了 Copy-and-Paste Networks 模型，以实现视频修复和增强，能够从与目标帧对应的参考帧中复制相关内容并粘贴填充，从而提高视频修复效果和车道检测精度。