A Unified Model for Image Colorization

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• 作者：Fabien Pierre (16) (17) (18) (19)
  Jean-Fran莽ois Aujol (16) (17)
  Aur茅lie Bugeau (18) (19)
  Vinh-Thong Ta (19) (20)
  
  16. University of Bordeaux ; IMB ; UMR 5251 ; 33400 ; Talence ; France
  17. CNRS ; IMB ; UMR 5251 ; 33400 ; Talence ; France
  18. University of Bordeaux ; LaBRI ; UMR 5800 ; 33400 ; Talence ; France
  19. CNRS ; LaBRI ; UMR 5800 ; 33400 ; Talence ; France
  20. IPB ; LaBRI ; UMR 5800 ; 33600 ; Pessac ; France
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8927
• 期：1
• 页码：297-308
• 全文大小：9,664 KB
• 参考文献：1. Gonzales, RC, Wintz, P (1987) Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA
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• 作者单位：Computer Vision - ECCV 2014 Workshops
• 丛书名：978-3-319-16198-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This paper addresses the topic of image colorization that consists in converting a gray-scale image into a color one. In the literature, there exist two main types of approaches to tackle this problem. The first one is the manual methods where the color information is given by some scribbles drawn by the user on the image. The interest of these approaches comes from the interactions with the user that can put any color he wants. Nevertheless, when the scene is complex many scribbles must be drawn and the interactive process becomes tedious and time-consuming. The second category of approaches is the exemplar-based methods that require a color image as input. Once the example image is given, the colorization is generally fully automatic. A limitation of these methods is that the example image needs to contain all the desired colors in the final result. In this paper, we propose a new framework that unifies these two categories of approaches into a joint variational model. Our approach is able to take into account information coming from any colorization method among these two categories. Experiments and comparisons demonstrate that the proposed approach provides competitive colorization results compared to state-of-the-art methods.