Single-Image Fence Removal Using Deep Convolutional Neural Network
Takuro Matsui, Masaaki Ikehara · IEEE Access · 2019
In public spaces such as zoos and sports facilities, the presence of fences often annoys tourists and professional photographers. There is a demand for a post-processing tool to produce a non-occluded view from an image or video. This “de-fencing” task is divided into two stages: one to detect fence regions and the other to fill the missing part. For over a decade, various methods have been proposed for video-based de-fencing. However, only a few single-image-based methods are proposed. In this paper, we focus on single-image fence removal. Conventional approaches suffer from inaccurate and non-robust fence detection and inpainting due to less content information. To solve these problems, we combine novel methods based on a deep convolutional neural network (CNN) and classical domain knowledge in image processing. In the training process, we are required to obtain both fence images and corresponding non-fence ground truth images. Therefore, we synthesize natural fence images from real images. Moreover, spacial filtering processing (e.g. a Laplacian filter and a Gaussian filter) improves the performance of the CNN for detection and inpainting. Our proposed method can automatically detect a fence and generate a clean image without any user input. Experimental results demonstrate that our method is effective for a broad range of fence images.