We present a multi-scale neural filling-in model for brightness reconstruction of initial DoG filtered images. In contrast to the classical single-scale fillin9-in models it no longer requires an additional (luminance) signal to restore arbitraryimages. Moreover, it substantially reduces the computational cost of the reconstruction process. We present a multi-layered hierarchical neural network comparable to a Laplacian pyramid in which contrast measures are filled-in in dedicated frequencydomains. We show in simulations how this model operates on synthetic as well as on real-world images.
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