  Image de-fencing is one of the important aspects of recreational photography
in which the objective is to remove the fence texture present in an image and
generate an aesthetically pleasing version of the same image without the fence
texture. In this paper, we aim to develop an automated and effective technique
for fence removal and image reconstruction using conditional Generative
Adversarial Networks (cGANs). These networks have been successfully applied in
several domains of Computer Vision focusing on image generation and rendering.
Our initial approach is based on a two-stage architecture involving two cGANs
that generate the fence mask and the inpainted image, respectively. Training of
these networks is carried out independently and, during evaluation, the input
image is passed through the two generators in succession to obtain the
de-fenced image. The results obtained from this approach are satisfactory, but
the response time is long since the image has to pass through two sets of
convolution layers. To reduce the response time, we propose a second approach
involving only a single cGAN architecture that is trained using the
ground-truth of fenced de-fenced image pairs along with the edge map of the
fenced image produced by the Canny Filter. Incorporation of the edge map helps
the network to precisely detect the edges present in the input image, and also
imparts it an ability to carry out high quality de-fencing in an efficient
manner, even in the presence of a fewer number of layers as compared to the
two-stage network. Qualitative and quantitative experimental results reported
in the manuscript reveal that the de-fenced images generated by the
single-stage de-fencing network have similar visual quality to those produced
by the two-stage network. Comparative performance analysis also emphasizes the
effectiveness of our approach over state-of-the-art image de-fencing
techniques.
