Image inpainting by reducing edge blur and error accumulation
Wei Huang, Youqiang Li, Zhi Xu, Chong Huang · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) · 2022
Small scale damaged image inpainting is divided into iterative inpainting and interpolation-based image inpainting algorithms. In previous studies, it is found that there are some phenomena such as sawtooth effect, edge blur and so on. The sawtooth effect exists because the weight calculation of neighborhood pixels only considers the spatial information, ignores the image feature information. In addition, there are many factors leading to edge blur including the error accumulation of inpainted pixels, the low weight of gradient direction, and the inpainting priority of pixel. In this paper, we propose an image inpainting method based on Fast Marching Method (FMM). Firstly we use gradient direction and isophote direction to maintain the edge consistency, so as to improve the algorithm effect. Secondly we explore the confidence matrix to reduce the error accumulation. Finally we use the low rank of image to preprocess the image region to reduce the algorithm time complexity. The results of the experiment show that our improved method get well performance in inpainting procedure than the-state-of-the-art method.