Image Inpainting with Local Feature Extraction

Image inpainting is a reconfigurable reconstruction of detected (marked) areas. This study focuses on damaged images and key point-based attributes such as speeded-up robust features (SURF), scale invariant feature transformation (SIFT), Harris algorithm and features of maximally stable extremal regions (MSER) are used for this purpose. It is assumed that there are images, one of them is to be inpainted and another of them is relevant to be taken from different viewpoints or from different geometric transformation, and the Affine transformation differences between the image to be inpainted and the relevant image are determined. In line with the information obtained, these two images are restructured to be at the same scale and in the same position. Experimental results show that the damaged image is successfully inpainted depending on the images of different scales and angles.