  We present a nonlocal variational image completion technique which admits
simultaneous inpainting of multiple structures and textures in a unified
framework. The recovery of geometric structures is achieved by using general
convolution operators as a measure of behavior within an image. These are
combined with a nonlocal exemplar-based approach to exploit the self-similarity
of an image in the selected feature domains and to ensure the inpainting of
textures. We also introduce an anisotropic patch distance metric to allow for
better control of the feature selection within an image and present a nonlocal
energy functional based on this metric. Finally, we derive an optimization
algorithm for the proposed variational model and examine its validity
experimentally with various test images.
