Context-Based Automatic Local Image Enhancement

  • Sung Ju Hwang
  • Ashish Kapoor
  • Sing Bing Kang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


In this paper, we describe a technique to automatically enhance the perceptual quality of an image. Unlike previous techniques, where global statistics of the image are used to determine enhancement operation, our method is local and relies on local scene descriptors and context in addition to high-level image statistics. We cast the problem of image enhancement as searching for the best transformation for each pixel in the given image and then discovering the enhanced image using a formulation based on Gaussian Random Fields. The search is done in a coarse-to-fine manner, namely by finding the best candidate images, followed by pixels. Our experiments indicate that such context-based local enhancement is better than global enhancement schemes. A user study using Mechanical Turk shows that the subjects prefer contextual and local enhancements over the ones provided by existing schemes.


Input Image User Study Image Enhancement High Dynamic Range Enhancement Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Luo, Y., Tang, X.: Photo and Video Quality Evaluation: Focusing on the Subject. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 386–399. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Finlayson, G., Trezzi, E.: Shades of gray and colour constancy. In: 1st Conf. on Color Imaging, pp. 37–41 (2004)Google Scholar
  3. 3.
    van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Trans. on Image Processing 16(9) (2007)Google Scholar
  4. 4.
    Gehler, P.V., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: CVPR (2008)Google Scholar
  5. 5.
    Rosenberg, C., Minka, T., Ladsariya, A.: Bayesian color constancy with non-Gaussian models. In: NIPS (2003)Google Scholar
  6. 6.
    Kang, S.B., Kapoor, A., Lischinski, D.: Personalization of image enhancement. In: CVPR (2010)Google Scholar
  7. 7.
    Caicedo, J.C., Kapoor, A., Kang, S.B.: Collaborative personalization of image enhancement. In: CVPR (2011)Google Scholar
  8. 8.
    Bychkovsky, V., Paris, S., Chan, E., Durand, F.: Learning photographic global tonal adjustment with a database of input/output image pairs. In: CVPR (2011)Google Scholar
  9. 9.
    Reinhard, E., Ward, G., Pattanaik, S., Debevec, P., Heidrich, W., Myszkowski, K.: High Dynamic Range Imaging: Acquisition, Display, and Image-based Lighting, 2nd edn. Elsevier (Morgan Kaufmann) (2010)Google Scholar
  10. 10.
    Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM TOG and SIGGRAPH 21, 249–256 (2002)Google Scholar
  11. 11.
    Reinhard, E., Stark, M., Shirley, P., Ferwerda, J.: Photographic tone reproduction for digital images. ACM TOG and SIGGRAPH 21 (2002)Google Scholar
  12. 12.
    Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM TOG and SIGGRAPH 21 (2002)Google Scholar
  13. 13.
    Bae, S., Paris, S., Durand, F.: Two-scale tone management for photographic look. ACM TOG and SIGGRAPH 25 (2006)Google Scholar
  14. 14.
    Dale, K., Johnson, M.K., Sunkavalli, K., Matusik, W., Pfister, H.: Image restoration using online photo collections. In: ICCV (2009)Google Scholar
  15. 15.
    Torralba, A.: Contextual priming for object detection. IJCV 53, 169–191 (2003)CrossRefGoogle Scholar
  16. 16.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS (December 2006)Google Scholar
  17. 17.
    Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: SIFT Flow: Dense Correspondence across Different Scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 28–42. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Jain, P., Kulis, B., Dhillon, I., Grauman, K.: Online metric learning and fast similarity search. In: NIPS (2008)Google Scholar
  19. 19.
    Jain, P., Kulis, B., Grauman, K.: Fast image search for learned metrics. In: CVPR (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sung Ju Hwang
    • 1
  • Ashish Kapoor
    • 2
  • Sing Bing Kang
    • 2
  1. 1.The University of TexasAustinUSA
  2. 2.Microsoft ResearchRedmondUSA

Personalised recommendations