Image Inpainting for High-Resolution Textures using CNN Texture Synthesis
Pascal Laube, Michael Grunwald, Matthias O. Franz, Georg Umlauf

TL;DR
This paper introduces a CNN-based patch inpainting method for high-resolution textures that effectively maintains global coherence and detail, outperforming existing techniques in filling large regions.
Contribution
It presents a novel patch-based CNN inpainting approach that optimizes global and detail texture statistics for high-resolution images, addressing computational limitations.
Findings
Capable of filling large inpainting regions with high quality
Outperforms comparable methods in high-resolution texture inpainting
Uses texture summary statistics for reference patch look-up
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 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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Advanced Image Processing Techniques
