Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Inpainting for High-Resolution Textures using CNN Texture
  Synthesis   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.

7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th