An Image Inpainting is the art to reconstruct an image by removing scratch, noise, object or any other defect. Fill the region of
missing information from a signal using surrounding information and re-form signal is the basic work of Inpainting algorithms. There
are various methods to do image Inpainting like exemplar based image Inpainting in which patch of particular size is get selected and
that patch is used to fill missing part of an image by calculating highest priority. In this confidence value and data value that is
collectively known as priority value of that patch is calculated. And after this most similar patch from the source region is detected and
pasted on highest priority patch. Existing technique gives high computation time by searching similar patches in whole source region
again and again. Also target region needs to be selected by user. So proposing method can provide better computation efficiency on
traditional image Inpainting algorithm for automatic detection and removal of scratch in an image. In this approach whole image
clustered using Iterative Threshold Selection algorithm and scratch will be detect automatically. After detecting scratch automatically
highest priority patch will be calculated. Due to clustering highest priority patch need not to be search in whole source region again and
again, 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.