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.
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Install Torch: http://torch.ch/docs/getting-started.html#_
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Clone the repository
git clone https://github.com/leehomyc/High-Res-Neural-Inpainting.gitRun the Demo
cd High-Res-Neural-Inpainting
# This will populate the `./models/` folder with trained content models.
bash ./models/download_content_models.sh
# 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.luaIf 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}
}