Pascal Laube, Michael Grunwald, Matthias O. Franz, and Georg
Umlauf
Deep neural networks have been successfully applied to problems such as
image segmentation, image super-resolution, coloration and image
inpainting. In this work we propose the use of convolutional neural
networks (CNN) for image inpainting of large regions in high-resolution
textures. Due to limited computational resources processing
high-resolution images with neural networks is still an open problem.
Existing methods separate inpainting of global structure and the transfer
of details, which leads to blurry results and loss of global coherence in
the detail transfer step. Based on advances in texture synthesis using
CNNs we propose patch-based image inpainting by a CNN that is able to
optimize for global as well as detail texture statistics. Our method is
capable of filling large inpainting regions, oftentimes exceeding the
quality of comparable methods for high-resolution images. For reference
patch look-up we propose to use the same summary statistics that are used
in the inpainting process.
CCS Concepts: Computing methodologies --> Neural networks; Image
processing
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