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leehomyc/High-Res-Neural-Inpainting

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leehomyc / High-Res-Neural-Inpainting

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High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis


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High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis

teaser

This is the code for High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis. Given an image, we use the content and texture network to jointly infer the missing region. This repository contains the pre-trained model for the content network and the joint optimization code, including the demo to run example images. The code is adapted from the Context Encoders and CNNMRF. Please contact Harry Yang for questions regarding the paper or the code. Note that the code is for research purpose only.

Demo

  git clone https://github.com/leehomyc/High-Res-Neural-Inpainting.git
  • Download the pre-trained models for the content and texture networks and put them under the folder models/.

  • Run the Demo

  cd High-Res-Neural-Inpainting
  # This will use the trained model to generate the output of the content network
  th run_content_network.lua
  # This will use the trained model to run texture optimization
  th run_texture_optimization.lua
  # This will generate the final result
  th blend.lua

Citation

If you find this code useful for your research, please cite:

@article{yang2016high,
  title={High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis},
  author={Yang, Chao and Lu, Xin and Lin, Zhe and Shechtman, Eli and Wang, Oliver and Li, Hao},
  journal={arXiv preprint arXiv:1611.09969},
  year={2016}
}