High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
Lua
Latest commit 3f8d332 Feb 21, 2017 @leehomyc update blend
Permalink
Failed to load latest commit information.
examples remove DS_Store Feb 21, 2017
gt update Feb 21, 2017
images update Feb 10, 2017
models update models Feb 21, 2017
mylib update Feb 20, 2017
.gitignore remove DS_Store Feb 21, 2017
LICENSE Initial commit Jan 26, 2017
README.md update README Feb 21, 2017
blend.lua update blend Feb 21, 2017
mask.png update Feb 21, 2017
run_content_network.lua update content network Feb 21, 2017
run_texture_optimization.lua update Feb 21, 2017
transfer_CNNMRF_wrapper.lua update texture network Feb 21, 2017
util.lua update Feb 20, 2017

README.md

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}
}