  De-fencing is to eliminate the captured fence on an image or a video,
providing a clear view of the scene. It has been applied for many purposes
including assisting photographers and improving the performance of computer
vision algorithms such as object detection and recognition. However, the
state-of-the-art de-fencing methods have limited performance caused by the
difficulty of fence segmentation and also suffer from the motion of the camera
or objects. To overcome these problems, we propose a novel method consisting of
segmentation using convolutional neural networks and a fast/robust recovery
algorithm. The segmentation algorithm using convolutional neural network
achieves significant improvement in the accuracy of fence segmentation. The
recovery algorithm using optical flow produces plausible de-fenced images and
videos. The proposed method is experimented on both our diverse and complex
dataset and publicly available datasets. The experimental results demonstrate
that the proposed method achieves the state-of-the-art performance for both
segmentation and content recovery.
