Examplar Based Inpainting Mechanism Based On Sr Algorithm (International Journal of Engineering and Computer Science, 2015)
In digital image processing “filling the missing areas” is still area of concern. Although so many algorithms proposed in the literature to tackle this issue of “filling the missing areas”. A new framework is presented in this paper for examplar-based in painting. In the proposed literature, algorithms are mainly classified into two stages. Firstly inpainting is applied on the coarse version of the input image, latter hierarchical based super resolution algorithm is used to find the information on the missing areas. The unique thing of the proposed method is easier to inpaint low resolution than its counter part and creating a mask based on the priority of missing information. To make inpainting image less sensitive to the parameter, it has inpainted several times by different configurations. The output from the inpainting phase is efficiently combined using the novel loppy belief propagation and finally by using the super resolution algorithm details are recovered based on the dictionary building approach. The proposed literature results are compared with the different conventional methods, to prove that the proposed literature results more reliable and yield high performance in all concern areas.