Image Inpainting Based On Partial Differential Equation

Digital image inpainting, which is very important in the region of image processing, is to complete a painting by filling the useful information into the damaged domains of a given image.The main ideal of models of image inpainting based on partial differential equation (PDE) is to change the image inpaiting process into a series of PDEs or energy functional models, so we can deal with images with numerical iterations and intelligent optimization methods. This kind of algorithm only propagates the local useful information around the inpainting domain in the direction of the isophotes automatically, so it is applicable only for low level non-texture images.On the basis of some kinds of local PDE-based inpainting models, a nonlocal curvature-driven diffusion model for image inpainting is proposed by incorporating the nonlocal differential operators into the curvature-driven diffusion model in this paper. This method differs from the local one in that pixels of similar structures rather than pixels in the neighborhood (the case for local models) are utilized to estimate the lost pixels. This difference makes this new model performs very efficiently for inpainting images, especially textured images. The exciting result is also proved by a large number of numerical experiments.