Skip to main content
SpringerLink
Log in

Segmentation Based Tone-Mapping for High Dynamic Range Images

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6915))

  • 2237 Accesses

Abstract

In this paper, we present a novel segmentation based method for displaying high dynamic range image. We segment images into regions and then carry out adaptive contrast and brightness adjustment using global tone mapping operator in the local regions to reproduce local contrast and brightness and ensure better quality. We propose a weighting scheme to eliminate the boundary artifacts caused by the segmentation and decrease the local contrast enhancement adaptively in the uniform area to eliminate the noise introduced. We demonstrate that our methods are easy to use and a fixed set of parameter values produces good results for a wide variety of images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Spivak, A., Belenky, A., Fish, A., Yadid-Pecht, O.: Wide-dynamic-range CMOS image sensors—comparative performance analysis. IEEE Trans. on Electron Devices 56(11), 2446–2461 (2009)

    Article  Google Scholar 

  2. Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: Proc. ACM SIGGRAPH 1997, pp. 369–378 (1997)

    Google Scholar 

  3. Mitsunaga, T., Nayar, S.K.: Radiometric self calibration. In: Proceedings of the Computer Vision and Pattern Recognition, vol. 1, pp. 374–380 (1999)

    Google Scholar 

  4. Mann, M.S., Picard, R.W.: On being undigital with digital cameras: extending dynamic range by combining differently exposed pictures. In: Proceedings of the IS&T’s 48th Annual Conference, Society for Imaging Science and Technology, pp. 422–428 (1995)

    Google Scholar 

  5. Seetzen, H., Heidrich, W., Stuerzlinger, W., Ward, G., Whitehead, L., Trentacoste, M., Ghosh, A., Vorozcovs, A.: High Dynamic Range Display Systems. ACM Transactions on Graphics (Siggraph 2004) 23(3), 760–768 (2004)

    Article  Google Scholar 

  6. Ferwerda, J.A., Luka, S.: A high resolution high dynamic range display for vision research (abstract/poster). Vision Sciences Society, 8th Annual Meeting, Journal of Vision 9(8), 346a (2009)

    Google Scholar 

  7. Bandoh, Y., Qiu, G., Okuda, M., Daly, S., Aachyyy, T., Au, O.C.: Recent Advances in High Dynamic Range Imaging Technology. In: 2010 17th IEEE International Conference on Image Processing, ICIP (2010)

    Google Scholar 

  8. Reinhard, E., Ward, G., Pattanaik, S., Debevec, P.: High dynamic range imaging, pp. 223–323. Morgan Kaufmann Publisher, San Francisco (2006)

    Book  Google Scholar 

  9. Kang, S.B., Uyttendale, M., Winder, S., Szeliski, R.: High dynamic range video. ACM Transactions on Graphics 22(3), 319–325 (2003)

    Article  Google Scholar 

  10. Mantiuk, R., Krawczyk, G., Myszkowski, K., Seidel, H.-P.: Perception-motivated High Dynamic Range Video Encoding. In: Proc. of SIGGRAPH 2004, pp. 733–741 (2004)

    Google Scholar 

  11. Tumblin, J., Rushmeier, H.: Tone reproduction for realistic images. IEEE Computer Graphics and Applications 13, 42–48 (1993)

    Article  Google Scholar 

  12. Ward, G.: A contrast-based scalefactor for luminance display. In: Graphics Gems IV, pp. 415–421. Academic Press, London (1994)

    Chapter  Google Scholar 

  13. Ferwerda, J.A., Pattanaik, S.N., Shirley, P., Greenberg, D.P.: A model of visual adaptation for realistic image synthesis. In: Proceedings of the SIGGRAPH 1996, pp. 249–258 (1996)

    Google Scholar 

  14. Drago, F., Myszkowski, K., Annen, T., Chiba, N.: Adaptive Logarithmic Mapping For Displaying High Contrast Scenes. The Journal of Computer Graphics Forum 22(3), 419–426 (2003)

    Article  Google Scholar 

  15. Duan, J., Qiu, G., Finlayson, G.M.D.: Learning to display high dynamic range images. Pattern Recognition 40(10), 2641–2655 (2007)

    Article  MATH  Google Scholar 

  16. Pardo, A., Sapiro, G.: Visualization of high dynamic range images. IEEE Transactions on Image Processing 12(6), 639–647 (2003)

    Article  Google Scholar 

  17. Larson, G.W., Rushmeier, H., Piatko, C.: A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Trans. on Visualization and Computer Graphics 3, 291–306 (1997)

    Article  Google Scholar 

  18. Chiu, K., Herf, M., Shirley, P., Swamy, S., Wang, C., Zimmerman, K.: Spatially nonuniform scaling functions for high contrast images. In: Proc. Graphics Interface 1993, pp. 245–253 (1993)

    Google Scholar 

  19. Tumblin, J., Turk, G.: LCIS: A boundary hierarchy for detail preserving contrast reduction. In: Proc. of ACM SIGGRAPH 1999, pp. 83–90 (1999)

    Google Scholar 

  20. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph (special issue SIGGRAPH 2002) 21(3), 257–266 (2002)

    Google Scholar 

  21. Li, X., Lam, K., Shen, L.: An adaptivea lgorithm for the display of high-dynamic range images. Journal of Visual Communication and Image Representation 18(5), 397–405 (2007)

    Article  Google Scholar 

  22. Wang, J., Xu, D., Lang, C., Li, B.: An Adaptive Tone Mapping Method for Displaying High Dynamic Range Images. Journal of Information Science and Engineering (2010)

    Google Scholar 

  23. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image processing 6, 965–976 (1997)

    Article  Google Scholar 

  24. Reinhard, E., Stark, M., Shirley, P., Ferwerda, J.: Photographic tone reproduction for digital images. In: Proc. ACM SIGGRAPH 2002 (2002)

    Google Scholar 

  25. Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. In: Proc. ACM SIGGRAPH 2002 (2002)

    Google Scholar 

  26. Li, Y., Sharan, L., Adelson, E.H.: Compressing and companding high dynamic range images with subband architectures. ACM Transactions on Graphics 24(3), 836–844 (2005)

    Article  Google Scholar 

  27. Krawczyk, G., Myszkowski, K., Seidel, H.P.: Computational model of lightness perception in high dynamic range imaging. In: Rogowitz, B.E., Pappas, T.N., Daly, S.J. (eds.) Human Vision and Electronic Imaging XI (2006)

    Google Scholar 

  28. Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Transactions on Graphics 22(3), 646–653 (2006)

    Article  Google Scholar 

  29. Stevens, S.S., Stevens, J.C.: Brightness function: parametric effects of adaptation and contrast. Journal of the Optical Society of America 53 (1960)

    Google Scholar 

  30. Comanicu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)

    Article  Google Scholar 

  31. Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based segmentation algorithm. In: IJCV (2004)

    Google Scholar 

  32. Cour, T., Benezit, F., Shi, J.: Spectral Segmentation with Multiscale Graph Decomposition. IEEE International Conference on Computer Vision and Pattern Recognition, CVPR (2005)

    Google Scholar 

  33. Ren, X., Fowlkes, C., Malik, J.: Learning probabilistic models for contour completion in natural images. International Journal of Computer Vision 77, 47–63 (2008)

    Article  Google Scholar 

  34. Yang, A., Wright, J., Ma, Y., Sastry, S.: Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding 110(2), 212–225 (2008)

    Article  Google Scholar 

  35. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From Contours to Regions: An Empirical Evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  36. Duan, J., Qiu, G.: Fast Tone Mapping for High Dynamic Range Images. In: 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 847–850 (2004)

    Google Scholar 

  37. Yoshida, A., Mantiuk, R., Myszkowski, K., Seidel, H.P.: Analysis of reproducing real-word appearance on displays of varying dynamic range. In: EUROGRAPHICS 2006, vol. 25 (3) (2006)

    Google Scholar 

  38. Kuang, J., Yamaguchi, H., Liu, C., Johnson, G.M., Fairchild, M.D.: Evaluating HDR rendering algorithms. ACM Transactions on Applied Perception 4(2), 9 (2007)

    Article  Google Scholar 

  39. Cadík, M., Wimmer, M., Neumann, L., Artusi, A.: Evaluation of HDR Tone Mapping Methods using Essential Perceptual Attributes. Computers and Graphics (2008)

    Google Scholar 

  40. Kuang, J., Heckaman, R., Fairchild, M.D.: Evaluation of HDR tone-mapping algorithms using a high-dynamic-range display to emulate real scenes. Journal of the Society for Information Display 18(7), 461–468 (2010)

    Article  Google Scholar 

  41. http://people.csail.mit.edu/fredo/PUBLI/Siggraph2002/index.html#hdr

  42. http://www.seas.upenn.edu/~timothee/software/ncut_multiscale/ncut_multiscale.html

  43. Duan, J., Bressan, M., Dance, C., Qiu, G.: Tone-mapping high dynamic range images by novel histogram adjustment. Pattern Recognition 43(5), 1847–1862 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fudan University, Shanghai, China

    Qiyuan Tian

  2. Southwestern University of Finance and Economics, Chengdu, China

    Jiang Duan & Tao Peng

  3. Applied Science and Technology Research Institute, Hong Kong, China

    Min Chen

Authors
  1. Qiyuan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiang Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. DGA, 7-9 rue des mathurins, 92 221, Bagneux, France

    Jacques Blanc-Talon

  2. VITO-TAP, Boeretang 200, 2400, MOl, Belgium

    Richard Kleihorst

  3. Ghent University, St. -Pietersnieuwstraat 41, B9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, Epping,, Po Box 76, 1710, Sydney, NSW, Australia

    Dan Popescu

  5. Physics, University of Antwerp, Universiteitsplein 1; Building N, 2610, Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tian, Q., Duan, J., Chen, M., Peng, T. (2011). Segmentation Based Tone-Mapping for High Dynamic Range Images. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23687-7_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23686-0

  • Online ISBN: 978-3-642-23687-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation