Colorization Network

This code is the implementation of the “Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification” paper. It contains the pre-trained model and example usage code.

Colorization Network
  • Type: library
  • Version: Apr, 2016
  • Language: lua
  • License: CC-by-sa-nc 4.0
  • Dependencies torch, nn, image, nngraph
github download

Overview

This code provides an implementation of the research paper:

Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa
ACM Transaction on Graphics (Proc. of SIGGRAPH 2016), 2016

We learn to automatically color grayscale images with a deep network. Our network learns both local features and global features jointly in a single framework. Our approach can then be used on images of any resolution. By incorporating global features we are able to obtain realistic colorings with our model.

See our project page for more detailed information.

License

Copyright (C) <2016> <Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa>

This work is licensed under the Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or
send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Satoshi Iizuka, Waseda University
iizuka@aoni.waseda.jp, http://hi.cs.waseda.ac.jp/~iizuka/index_eng.html
Edgar Simo-Serra, Waseda University
esimo@aoni.waseda.jp, http://hi.cs.waseda.ac.jp/~esimo/

Dependencies

Each package can be installed via Luarocks:

luarocks install nn
luarocks install image
luarocks install nngraph

Usage

First, download the colorization model by running the download script:

sh download_model.sh

Basic usage is:

th colorize.lua <input_image> [<output_image>]

For example:

th colorize.lua ansel_colorado_1941.png out.png

Notes

  • This is developed on a linux machine running Ubuntu 14.04 during late 2015.
  • The provided code does not use GPU accelerated (trivial to change).
  • Please note that the model is slow on large images (over 512x512 pixels) and may run out of memory.

Citing

If you use this code please cite:

@Article{IizukaSIGGRAPH2016,
   author = {Satoshi Iizuka and Edgar Simo-Serra and Hiroshi Ishikawa},
   title = {{Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification}},
   journal = "ACM Transactions on Graphics (Proc. of SIGGRAPH 2016)",
   year = 2016,
   volume = 35,
   number = 4,
}