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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 purposesincluding assisting photographers and improving the performance of computervision algorithms such as object detection and recognition. However, thestate-of-the-art de-fencing methods have limited performance caused by thedifficulty of fence segmentation and also suffer from the motion of the cameraor objects. To overcome these problems, we propose a novel method consisting ofsegmentation using convolutional neural networks and a fast/robust recoveryalgorithm. The segmentation algorithm using convolutional neural networkachieves significant improvement in the accuracy of fence segmentation. Therecovery algorithm using optical flow produces plausible de-fenced images andvideos. The proposed method is experimented on both our diverse and complexdataset and publicly available datasets. The experimental results demonstratethat the proposed method achieves the state-of-the-art performance for bothsegmentation and content recovery.

Accurate and efficient video de-fencing using convolutional neural networks and temporal information