Multi-scale image inpainting with label selection based on local statistics

In this paper, we proposed a novel inpainting method where we use a multi-scale approach to speed up the well-known Markov Random Field (MRF) based inpainting method. MRF based inpainting methods are slow when compared with other exemplar-based methods, because its computational complexity is O( ²) (feasible solutions' labels). Our multi-scale approach seeks to reduce the number of the (feasible) labels by an appropriate selection of the labels using the information of the previous (low resolution) scale. For the initial label selection we use local statistics; moreover, to compensate the loss of information in low resolution levels we use features related to the original image gradient. Our computational results show that our approach is competitive, in terms reconstruction quality, when compare to the original MRF based inpainting, as well as other exemplarbased inpaiting algorithms, while being at least one order of magnitude faster than the original MRF based inpainting and competitive with exemplar-based inpaiting.