Interpolation-Based Image Inpainting in Color Images Using High Dimensional Model Representation

Image inpainting is the process of filling missing or fixing corrupted regions in a given image. The intensity values of the pixels in missing area are expected to be associated with the pixels in the surrounding area. Interpolation-based methods that can solve the problem with a high accuracy may become inefficient when the dimension of the data increases. We solve this problem by representing images with lower dimensions using High Dimensional Model Representation method. We then perform Lagrange interpolation on the lower dimensional data to find the intensity values of the missing pixels. In order to use High Dimensional Model Representation method and to improve the accuracy of Lagrange interpolation, we also propose a procedure that decompose missing regions into smaller ones and perform inpainting hierarchically starting from the smallest region. Experimental results demonstrate that the proposed method produces better results than the variational and exemplar-based inpainting approaches in most of the test images.