A Curvature-Driven Image Inpainting Approach for High-Density Impulse Noise Removal

Abstract

A PDE-based image inpainting method is proposed in this work for removing high-density impulse noise in images. In this model, the diffusion or inpainting process is driven by the difference curvature of the level curve. The proposed framework has two stages. In the first stage the noisy pixels are detected and they are piped to the second stage. In the second stage, these noisy pixels are inpainted using the information from their neighborhood. The connectivity principle is well realized and the edges and fine details are preserved well by the proposed model. The proposed method is compared (in terms of denoising capability) with the state-of-the-art impulse denoising models. The performance is quantified in terms of statistical quality measures. It is observed that the proposed method is capable of restoring images corrupted with high-density impulse noise (even up to 90 %). The experiments clearly demonstrate the effective restoration capacity of the proposed image inpainting model. 2014 King Fahd University of Petroleum and Minerals.