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Image Inpainting for High-Resolution Textures using CNN Texture Synthesis | | Pascal Laube
; Michael Grunwald
; Matthias O. Franz
; Georg Umlauf
; | | Date: |
8 Dec 2017 | | Abstract: | 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 single
network topology that is able to optimize for global as well as detail texture
statistics. Our method is capable of filling large inpainting regions,
oftentimes exceeding quality of comparable methods for images of
high-resolution (2048x2048px). For reference patch look-up we propose to use
the same summary statistics that are used in the inpainting process. | | Source: | arXiv, 1712.3111 | | Services: | Forum | Review | PDF | Favorites | |
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