Examplar-Based Inpainting Model For The Big Scale Image Inpainting Model

Image Inpainting algorithms originated in the Renaissance period. The extensivefocus on the technology concerning the digital images is a product of the digitizationin the recent years. In the inpaiting progress, we have to change the unknown regions,to transit naturally and continuously from the known regions in the image to theinpaiting regions.At present, we haven’t got any satisfactory algorithms to inpaint the large-scaleregions in the images. And this paper first reviews the past classical algorithms toresolve this problem, and then combines the idea--nonlocal means and scale invariantfeature transform (SIFT) algorithm, and finally proposes a new exemplar-basedmethod for the large-scale regions in the images with periodicity.This algorithm takes into consideration that the specific patch in the unknownregion could also be quite similar with some patches in the known regions afterrotation and tension. In this sense, these patches can also be chosen as exemplars forthat unknown patch. In the filling process of the unknown regions, this paper usesseveral possible candidate patches rather than mere one patch in the original model,to prevent the possible noises brought by the latter model and improve the robustness.At last, the weight for every candidate patch is given by the idea of nonlocal means.After the application of the algorithm present in this paper to the images withlarge-scale unknown regions and high periodicity, the experiment results indicate thatthis algorithm can get a visually better result than the exemplar-based model byCrimisi and et al.