A Comparative Analysis of Exemplar Based Image Inpainting Algorithms

Image inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. Many researchers have proposed a large variety of exemplar based image inpainting algorithms to restore the structure and texture of damaged images. However no recent study has been undertaken for a comparative evaluation of these algorithms. In this paper, we are comparing various exemplar based image inpainting algorithms which do not have diffusion related blur in the result image. The analyzed algorithms are Antonio Criminisi et al’s Region filling algorithm, Jiying Wu et al’s Hybrid algorithm and Zhaolin Lu et al’s Image completion algorithm. Both theoretical analysis and experiments have made to analyze the results of these exemplar based image inpainting algorithms on the basis of Peak Signal to Noise Ratio (PSNR).