Main Article Content
In this paper we intend to illustrate a utility and application of Kriging approximations in image processing problem designated by inpainting or filling in. We also review three state of the art infilling algorithms that deal with higher order PDE, Total Variation and exemplar-based approach. The computer model, a simple idea, we propose addresses this problem in deterministic way, and thus a response from a model lacks random error, i.e., repeated runs for the same input parameters gives the same response from the model. In its simple sense, Kriginng problem is related to the more general problem of predicting output from a computer model at untried inputs. Hence it lends it self for solving inpainting problem. Experimental results show that the proposed model yields qualitative results that are comparable to the existing complex approaches. The proposed method is very effective and simple to fill small gaps.
How to Cite
, R. M. (2018). New similarity Measure for Exemplar Based in Painting. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(1), 247–251. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1000