Enhancing Underexposed Images Preserving the Original Mood

  • Silvia Corchs
  • Francesca Gasparini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)


In the present article we focus on enhancing the contrast of images with low illumination that present large underexposed regions. Most of these images represent night images. When applying standard contrast enhancement techniques, usually the night mood is modified, and also a noise over-enhancement within the darker regions is introduced. In a previous work we have described our local contrast correction algorithm designed to enhance images where both underexposed and overexposed regions are simoultaneously present. Here we show how this algorithm is able to automatically enhance night images, preserving the original mood. To further improve the performance of our method we also propose here a denoising procedure where the strength of the smoothing is a function of an estimated level of noise and it is further weighted by a saliency map. The method has been applied to a proper database of outdoor and indoor underexposed images. Our results have been qualitatively compared with well know contrast correction methods.


local contrast enhancement underexposed images night images 


  1. 1.
    Aja-Fernndez, S., Vegas-Snchez-Ferreroa, G., Martn-Fernndez, M., Alberola-Lpez, C.: Automatic noise estimation in images using local statistics. additive and multiplicative cases. Image and Vision Computing 27, 756–770 (2009)CrossRefGoogle Scholar
  2. 2.
    Arici, T., Dikbas, S., Altunbasa, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing 18, 1921–1935 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bruna, A., Naccari, F., Gasparini, F., Schettini, R.: Multidomain pixel analysis for illuminant estimation. In: Proc. of SPIE Digital Photography II, vol. 6069, pp. 115–122 (2006)Google Scholar
  4. 4.
    Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Simul. 4, 490–530 (2005)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Ciocca, C., Schettini, R.: Multiple image thumbnailing. Proceedings of SPIE Digital Photography VI, vol. 7537, p. 75370S (2010)Google Scholar
  6. 6.
    Frankle, J., McCann, J.: Method and apparatus for lightness imaging. US Patent 4, 384, 386 (1983)Google Scholar
  7. 7.
    Gasparini, F., Corchs, S., Schettini, R.: Low quality image enhancement using visual attention. Optical Engineering letters 46, 040502 (2007)CrossRefGoogle Scholar
  8. 8.
    Immerkaer, J.: Fast noise variance estimation. Computer Vision and Image Understanding 64, 300–302 (1996)CrossRefGoogle Scholar
  9. 9.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Freeman, W., Szeliski, R., Kang, S.B.: Noise estimation from a single image. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 901–908 (2006)Google Scholar
  11. 11.
    Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 299–314 (2008)CrossRefGoogle Scholar
  12. 12.
    Ma, Y., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proc. of the Eleventh ACM International Conference on Multimedia, pp. 374–381 (2003)Google Scholar
  13. 13.
    Moroney, N.: Local colour correction using non-linear masking. In: IS&T/SID Eighth Color Imaging Conference, pp. 108–111 (2000)Google Scholar
  14. 14.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  15. 15.
    Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: Image denoising using scale mixtures of Gaussians in the wavelet domain 12, 1338–1351 (2003)Google Scholar
  16. 16.
    Schettini, R., Gasparini, F., Corchs, S., Marini, F., Capra, A., Castorina, A.: A contrast image correction method. Journal of Electronic Imaging 19, 023005 (2010)CrossRefGoogle Scholar
  17. 17.
    Stark, A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing 9, 889–896 (2000)CrossRefGoogle Scholar
  18. 18.
    Tai, S., Yang, S.: A fast method for image noise estimation using laplacian operator and adaptive edge detection. In: Communications, Control and Signal Processing ISCCSP, pp. 1077–1081 (2008)Google Scholar
  19. 19.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proc. IEEE Int. Conf. Computer Vision, pp. 839–846 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Silvia Corchs
    • 1
  • Francesca Gasparini
    • 1
  1. 1.DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione)University of Milano-BicoccaMilanoItaly

Personalised recommendations