Optimising Data for Exemplar-Based Inpainting

Optimisation of inpainting data plays an important role in inpainting-based codecs. For diffusion-based inpainting, it is well-known that a careful data selection has a substantial impact on the reconstruction quality. However, for exemplar-based inpainting, which is advantageous for highly textured images, no data optimisation strategies have been explored yet. In our paper, we propose the first data optimisation approach for exemplar-based inpainting. It densifies the known data iteratively: New data points are added by dithering the current error map. Afterwards, the data mask is further improved by nonlocal pixel exchanges. Experiments demonstrate that our method yields significant improvements for exemplar-based inpainting with sparse data.

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