Image De-Fencing Revisited

We introduce a novel image defencing method suitable for consumer photography, where plausible results must; be achieved under common camera settings. First, detection of lattices with see-through texels is performed in an iterative process using online learning and classification from intermediate results to aid subsequent detection. Then, segmentation of the foreground is performed using accumulated statistics from all lattice points. Next, multi-view inpainting is performed to fill in occluded areas with information from shifted views where parts of the occluded regions may be visible. For regions occluded in all views, we use novel symmetry-augmented inpainting, which combines traditional texture synthesis with an increased pool of candidate patches found by simulating bilateral symmetry patterns from the source image. The results show the effectiveness of our proposed method.