research-article

Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification

Authors:

Satoshi Iizuka,

Edgar Simo-Serra,

Hiroshi IshikawaAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 35, Issue 4

Article No.: 110, Pages 1 - 11

https://doi.org/10.1145/2897824.2925974

Published: 11 July 2016 Publication History

Abstract

We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image. The entire framework, including the global and local priors as well as the colorization model, is trained in an end-to-end fashion. Furthermore, our architecture can process images of any resolution, unlike most existing approaches based on CNN. We leverage an existing large-scale scene classification database to train our model, exploiting the class labels of the dataset to more efficiently and discriminatively learn the global priors. We validate our approach with a user study and compare against the state of the art, where we show significant improvements. Furthermore, we demonstrate our method extensively on many different types of images, including black-and-white photography from over a hundred years ago, and show realistic colorizations.

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Index Terms

Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 35, Issue 4

July 2016

1396 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2897824

Issue’s Table of Contents

Copyright © 2016 ACM.

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Publication History

Published: 11 July 2016

Published in TOG Volume 35, Issue 4

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https://doi.org/10.1177/30504554241297613
Fodor KAndrás R(2025)Enhancing NetVLAD Based localization with Gaussian Splat2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI)10.1109/SAMI63904.2025.10883320(000359-000364)Online publication date: 23-Jan-2025
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