image inpainting by global structure and texture propagation
Image inpainting is a technique to repair damaged images or modify images in a non-detectable form. In this paper, a novel global algorithm for region filling is proposed for image inpainting. After removing objects from an image, our approach fills the regions using patches taken from the image. The filling process is formulated as an energy minimization problem by Markov random fields (MRFs) and the belief propagation (BP) is utilized to solve the problem. Our energy function includes structure and texture information obtained from the image. One challenge in using BP is that its computational complexity is the square of the number of label candidates. To reduce the large number of label candidates, we present a coarse-to-fine scheme where two BPs run with much smaller numbers of label candidates instead of one BP running with a large number of label candidates. Experimental results demonstrate the excellent performance of our algorithm over other related algorithms.
inpainting markov random fields belief propagation image completion