skip to main content
article

Colorization using optimization

Published:01 August 2004Publication History
Skip Abstract Section

Abstract

Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task.In this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking. Our method is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input.

References

  1. BURNS, G. Colorization. Museum of Broadcast Communications: Encyclopedia of Television, http://www.museum.tv/archives/etv/index.html.Google ScholarGoogle Scholar
  2. COOPER, R. 1991. Colorization and moral rights: Should the United States adopt unified protection for artists? Journalism Quarterly (Urbana, Illinois), Autumn.Google ScholarGoogle Scholar
  3. JACK, K. 2001. Video Demystified, 3rd edition ed. Elsevier Science & Technology.Google ScholarGoogle Scholar
  4. LUCAS, B., AND KANADE, T. 1981. An iterative image registration technique with an application to stereo vision. In Proc. Int. Joint Conf. AI 674--679.Google ScholarGoogle Scholar
  5. MARKLE, W., AND HUNT, B., 1987. Coloring a black and white signal using motion detection. Canadian patent no. 1291260, Dec.Google ScholarGoogle Scholar
  6. NEURALTEK, 2003. BlackMagic photo colorization software, version 2.8 http://www.timebrush.com/blackmagic.Google ScholarGoogle Scholar
  7. PERONA, P., AND MALIK, J. 1989. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on PAMI 8, 5, 565--593.Google ScholarGoogle Scholar
  8. PRESS, W., TEUKOLSKY, S., VETTERLING, W., AND FLANNERY, B. 1992. Numerical Recipes in C: The art of scientific computing. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. SHI, J., AND MALIK, J. 1997. Normalized cuts and image segmentation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 731--737. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. SILBERG, J., 1998. The Pleasantville post production team that focussed on the absence of color. Cinesite Press Article, http://www.cinesite.com/core/press/articles/1998/10_00_98-team.html.Google ScholarGoogle Scholar
  11. TANG, B., SAPIRO, G., AND CASSELES, V. 2001. Color image enhancement via chromaticity diffusion. IEEE Transactions on Image Processing 10, 5, 701--708. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. TORRALBA, A., AND FREEMAN, W. T. 2003. Properties and applications of shape recipes. In IEEE Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  13. WEISS, Y. 1999. Segmentation using eigenvectors: A unifying view. In Proceedings ICCV, 975--982. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. WELSH, T., ASHIKHMIN, M., AND MUELLER, K. 2002. Transferring color to greyscale images. ACM Transactions on Graphics 21, 3 (July), 277--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. ZOMET, A., AND PELEG, S. 2002. Multi-sensor super resolution. In Proceedings of the IEEE Workshop on Applications of Computer Vision. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Colorization using optimization

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 23, Issue 3
      August 2004
      684 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/1015706
      Issue’s Table of Contents

      Copyright © 2004 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 August 2004
      Published in tog Volume 23, Issue 3

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader