An interpolation-based texture and pattern preserving algorithm for inpainting color images

Image inpainting can be defined as the process of filling missing regions in a given image with appropriate intensities. Intensity values of pixels in a 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. Also, they suffer from finding the underlying texture and pattern in the missing region. In this study, we propose a texture and pattern preserving interpolation-based algorithm for inpainting missing regions in color images. First, the proposed approach produces candidate inpainting results by interpolating to the observed data at the different neighborhoods of the missing region using High Dimensional Model Representation with Lagrange interpolation. Later, a final inpainting decision is given among the candidates for each pixel in the missing region for a texture and pattern preserving inpainting. This is achieved by combining the information obtained from co-occurrence matrix and from a patch found in the image that fits best to the missing region. We evaluate the performance of the proposed approach on various color images that include different texture and pattern. We also compare the proposed approach with the state-of-the-art inpainting methods in the literature. Experimental results demonstrate the potential of the proposed approach.