ITS Cluster Based Efficient Exemplar Image Inpainting Technique (International Journal of Engineering and Computer Science, 2015)
An Image Inpainting is the art to reconstruct an image by removing scratch, noise, object or any other defect. Fill the region ofmissing information from a signal using surrounding information and re-form signal is the basic work of Inpainting algorithms. Thereare various methods to do image Inpainting like exemplar based image Inpainting in which patch of particular size is get selected andthat patch is used to fill missing part of an image by calculating highest priority. In this confidence value and data value that iscollectively known as priority value of that patch is calculated. And after this most similar patch from the source region is detected andpasted on highest priority patch. Existing technique gives high computation time by searching similar patches in whole source regionagain and again. Also target region needs to be selected by user. So proposing method can provide better computation efficiency ontraditional image Inpainting algorithm for automatic detection and removal of scratch in an image. In this approach whole imageclustered using Iterative Threshold Selection algorithm and scratch will be detect automatically. After detecting scratch automaticallyhighest priority patch will be calculated. Due to clustering highest priority patch need not to be search in whole source region again andagain, it just need to take most similar patch from this cluster. So due to this source region can be minimized for optimal patch to search.So proposing method can provides better speedy approach for removal of scratch in an image than existing method.