Wavelet contrast-based image inpainting with sparsity-driven initialization

Image inpainting is the task of removing undesired objects or flaws in images. This work advances an exemplar-based global optimization image inpainting algorithm. For that purpose, the inpainting area is iteratively refined through the minimization of a cost function. The minimization outcome depends on the initial values of the inpainting area. We compare three initialization methods with a new sparsity-driven approach. Lastly, we propose the new wavelet contrast costs which increase the inpainting quality. Wavelet contrasts reduce computational complexity in comparison to wavelet histograms while preserving their ability of measuring the density of image texture.