Contour-guided image completion using a sample image

Abstract. Image completion techniques are required to fill in missing regions in digital images. We propose a method that focuses on the complex structure of the foreground object. We observe that if we match the complete deformable contour of the sample object and obtain the missing contour (i.e., point set positions), the structure of the damaged object can be reconstructed. We further find that the reconstructed contour provides sufficient information to complete the image, which generates contour guidance maps as soft constraints. With the contour guidance maps, we then fill in the incomplete image parts by combining textures of both the damaged image and the sample object using optimization techniques. Experimental results demonstrate that our method is a potentially superior method for filling target regions of incomplete foreground objects with complex structure.