  Conventional approaches to image de-fencing use multiple adjacent frames for
segmentation of fences in the reference image and are limited to restoring
images of static scenes only. In this paper, we propose a de-fencing algorithm
for images of dynamic scenes using an occlusion-aware optical flow method. We
divide the problem of image de-fencing into the tasks of automated fence
segmentation from a single image, motion estimation under known occlusions and
fusion of data from multiple frames of a captured video of the scene.
Specifically, we use a pre-trained convolutional neural network to segment
fence pixels from a single image. The knowledge of spatial locations of fences
is used to subsequently estimate optical flow in the occluded frames of the
video for the final data fusion step. We cast the fence removal problem in an
optimization framework by modeling the formation of the degraded observations.
The inverse problem is solved using fast iterative shrinkage thresholding
algorithm (FISTA). Experimental results show the effectiveness of proposed
algorithm.
