Highly Efficient Local Non-Texture Image Inpainting Based on Partial Differential Equation

Image in painting has been a popular study point in recent years and a number of strategies have been developed. Partial differential equation (PDE) image in painting approach often acts as a fundamental building block in this area. However, the high computing load limits the application of PDE-based image in painting, especially in mobile terminal. In this paper, first an enhanced Curvature-Driven Diffusions (ECDD) model is proposed to improve the repairing performance. Then a fast local non-texture in painting scheme is performed based on ECDD and total variation (TV) to make the computing of the PDE-based image in painting more efficient. The experimental results show that the proposed strategy not only can repair the long disconnected objects more accurately, but also can greatly shorten the iteration time of image in painting.