  In recent times, the availability of inexpensive image capturing devices such
as smartphones/tablets has led to an exponential increase in the number of
images/videos captured. However, sometimes the amateur photographer is hindered
by fences in the scene which have to be removed after the image has been
captured. Conventional approaches to image de-fencing suffer from inaccurate
and non-robust fence detection apart from being limited to processing images of
only static occluded scenes. In this paper, we propose a semi-automated
de-fencing algorithm using a video of the dynamic scene. We use convolutional
neural networks for detecting fence pixels. We provide qualitative as well as
quantitative comparison results with existing lattice detection algorithms on
the existing PSU NRT data set and a proposed challenging fenced image dataset.
The inverse problem of fence removal is solved using split Bregman technique
assuming total variation of the de-fenced image as the regularization
constraint.
