Coloring the monochrome days: a new technique for colorization of grayscale images

A research group led by Professor Hiroshi Ishikawa and Junior Researchers Satoshi Iizuka and Edgar Simo-Serra of the Faculty of Science and Engineering at Waseda University established a technique for automatically colorizing grayscale images by using convolutional neural networks, a type of deep learning by artificial intelligence.

本技術をもとに彩色された大隈重信と来訪者(大隈邸温室内) 大学史資料センター写真データベースより

Colorized photograph of Shigenobu Okuma and guests, the Okuma Greenhouse, 1910s

米国前大統領候補ブライアン一行の大隈訪問(大隈邸温室内)  大学史資料センター写真データベースより

Previous American presidential candidate Brian in the center, the Okuma Greenhouse, Oct. 18, 1905

A01-18c

Shigenobu Okuma, Taisho period

本技術をもとに彩色された図書館全景 大学史資料センター写真データベースより

The old library (now Building 2), 1933

本技術をもとに彩色された大隈邸内温室の草花 大学史資料センター写真データベースより

Inside the Okuma Greenhouse, the end of the Meiji period

The colorization architecture of this model is divided into four parts: a low-level features network, a mid-level features network, a global features network, and a colorization network. This colorization method differs from previous ones for it is completely automatic and does not require any reference images.

 

図1ディープネットワークを用いた大域特徴と局所特徴の学習による色付けのモデル構造

This technique, partially supported by Japan Science and Technology CREST Program, will allow natural coloring of different kinds of images, including monochrome photographs dating back 100 years. The results of this research will be presented at an international computer graphics convention, SIGGRAPH 2016, in Los Angeles in July.

The source code is available on GitHub: https://github.com/satoshiiizuka/siggraph2016_colorization

Images from the Waseda University Archives

For more information, visit the project’s website or
contact Professor Hiroshi Ishikawa (e-mail: hfs@waseda.jp)

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