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Image Inpainting for High-Resolution Textures using CNN Texture Synthesis : Short Paper

  • 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.

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Author:Pascal Laube, Matthias O. FranzORCiDGND, Michael Grunwald, Georg UmlaufORCiDGND
Parent Title (English):Computer Graphics & Visual Computing (CGVC) 2018, 13th - 14th September 2018, Swansea University, United Kingdom
Document Type:Conference Proceeding
Year of Publication:2018
Release Date:2019/01/18
Edition:Version 2
Page Number:5
Institutes:Institut für Optische Systeme - IOS
Open Access?:Ja
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)