Deep Structured Energy-Based Image Inpainting
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README.md
inpaint.py
utils.py

README.md

Deep Structured Energy-Based Image Inpainting

Fazil Altinel, Mete Ozay, Takayuki Okatani - http://www.vision.is.tohoku.ac.jp/

If you make use of this code, please cite the following paper:

@INPROCEEDINGS{altinel2018dsebii, 
author={F. Altinel and M. Ozay and T. Okatani}, 
booktitle={2018 24th International Conference on Pattern Recognition (ICPR)}, 
title={Deep Structured Energy-Based Image Inpainting}, 
year={2018}, 
volume={}, 
number={}, 
pages={423-428},
doi={10.1109/ICPR.2018.8546025}, 
ISSN={1051-4651}, 
month={Aug},}

Overview

This repository contains TensorFlow implementation of "Deep Structured Energy-Based Image Inpainting" paper (accepted to ICPR 2018).

  • Network Architecture:
Input(x)  -> CONV1(KernelSize=8, NumFilter= 32, Stride=4) -> CONV2(KernelSize=4, NumFilter= 64, Stride=2) -> CONV3(KernelSize=3, NumFilter= 64, Stride=1) -> FC1(512)
                                                                                                                                                                      > Energy_x(y^)
Input(y^) -> CONV1(KernelSize=8, NumFilter= 32, Stride=4) -> CONV2(KernelSize=4, NumFilter= 64, Stride=2) -> CONV3(KernelSize=3, NumFilter= 64, Stride=1) -> FC1(512)
  • Learning rates that used during training:
For energy update: Learning rate = 0.01, momentum = 0.9.
For parameter update: Learning rate = 0.001.

Files

files/
├── imgs/ - Test images folder
├── model/ - Model files folder
└── results/ - Test results folder
inpaint.py - Loads the model file and generates inpainted image(s) for given image(s).
utils.py - Various utilities for 'inpaint.py'

Dependencies

Tests are performed with following version of libraries:

  • Python 3.4
  • Numpy 1.11.3
  • TensorFlow 1.0.1

Running

Download CelebA dataset (Align&Cropped Images): http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html.

Download the model file trained on CelebA dataset: http://vision.is.tohoku.ac.jp/~altinel/uploadFiles/celebA.tar.gz. Extract and locate the files under files/model/.

Run the command below for all testing set of CelebA dataset:

$ python inpaint.py --allTest 1 --allImagesPath /path/to/all/dataset/folder/

Run the command below for testing images under files/imgs/:

$ python inpaint.py --allTest 0 --allImagesPath /path/to/all/dataset/folder/ --testImagesPath files/imgs/

Result images will be located under files/results/.

License

The source code is licensed under GNU General Public License v3.0.