International Journal of Computer Vision

, Volume 69, Issue 1, pp 109–117 | Cite as

Visual Acuity in Day for Night

  • Gloria HaroEmail author
  • Marcelo Bertalmío
  • Vicent Caselles
Short Papers


In film production, it is sometimes not convenient or directly impossible to shoot some night scenes at night. The film budget, schedule or location may not allow it. In these cases, the scenes are shot at daytime, and the ‘night look’ is achieved by placing a blue filter in front of the lens and under-exposing the film. This technique, that the American film industry has used for many decades, is called ‘Day for Night’ (or ‘American Night’ in Europe.) But the images thus obtained don’t usually look realistic: they tend to be too bluish, and the objects’ brightness seems unnatural for night-light. In this article we introduce a digital Day for Night algorithm that achieves very realistic results. We use a set of very simple equations, based on real physical data and visual perception experimental data. To simulate the loss of visual acuity we introduce a novel diffusion Partial Differential Equation (PDE) which takes luminance into account and respects contrast, produces no ringing, is stable, very easy to implement and fast. The user only provides the original day image and the desired level of darkness of the result. The whole process from original day image to final night image is implemented in a few seconds, computations being mostly local.


day for night visual perception dark adaptation non-linear diffusion 


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  1. Alvarez, L., Guichard, F., Lions, P.L., and Morel, J.M. 1993. Axioms and fundamental equations of image processing. Archive Rat. Mech. and Anal., 200–257.Google Scholar
  2. Bénilan, P. and Crandall, M. 1981. The continuous dependence on φ of solutions of ut - δ φ(u) = 0.Indiana Math. J., 30(2):161–177.CrossRefzbMATHGoogle Scholar
  3. Bertalmío, M., Fort, P., and Sànchez-Crespo, D. 2004. Real-time, accurate Depth of Field using Anisotropic Diffusion and Programmable Graphics Cards.In Proceedings of 2nd 3DPVT. IEEE Computer Society Press.Google Scholar
  4. Cornsweet, T.N. and Yellott, J.I. 1985. Intensity-independent spatial summation. Journal of Optical Society of America, 2(10):1769–1786.MathSciNetCrossRefGoogle Scholar
  5. Durand, F. and Dorsey, J. 2000. Interactive Tone Mapping.In Proc. Eurographics Workshop on Rendering. Springer Verlag.Google Scholar
  6. Ferwerda, J.A., Pattanaik, S.N., Shirley, P., and Greenberg, D.P. 1996. A Model of Visual Adaptation for Realistic Image Synthesis.In Proceedings of SIGGRAPH 1996, pp. 249–258. ACM Press/ACM SIGGRAPH.Google Scholar
  7. Finlayson, G., Hordley, S., and Drew, M. 2002. Removing shadows from images.In ECCV 2002.Google Scholar
  8. Guichard, F. and Morel, J. 2000. Image Iterative Smoothing and P. D.E.‘s’. Book in preparation.Google Scholar
  9. Hunt 1952. Light and Dark Adaptation in the Perception of Color. J. Optical Soc. America A, 42(3):190.Google Scholar
  10. Lumet, S. 1995. Making Movies.Alfred A. Knopf.Google Scholar
  11. Maloney, L.T. 1986. Evaluation of linear models of surface spectral reflectance with small numbers of parameters. J. Optical Soc. America-A, 3(10):1673–1683.Google Scholar
  12. Massey, P. and Foltz, C.B. 2000. The Spectrum of the Night Sky Over Mount Hopkins and Kitt Peak: Changes after a decade. Publications of the Astronomical Society of the Pacific, 112(566).Google Scholar
  13. Perona, P. and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE TPAMI, 12(7):629–639.Google Scholar
  14. Ramanath, R., Kuehni, R., Snyder, W., and Hinks, D. 2004. Spectral spaces and color Spaces. Color Research and Application, 29(1):29–37.CrossRefGoogle Scholar
  15. Slater, D. and Healey, G. 1997. Using a spectral reflectance model for the illumination-invariant recognition of local image structure. IEEE TPAMI, 19(10).Google Scholar
  16. Stabell, B. and Stabell, U. 2002. Effects of rod activity on color perception with light adaptation. Journal of Optical Society of America, 19(7):1249–1258.Google Scholar
  17. Thompson, W., Shirley, P., and Ferwerda, J. 2002. A Spatial Post-Processing Algorithm for Images of Night Scenes. Journal of Graphics Tools, 7(1):1–12.Google Scholar
  18. Tumblin, J. and Rushmeier, H. 1993. Tone Reproduction for Realistic Images. IEEE Computer Graphics and Applications, 13(6):42–48.CrossRefGoogle Scholar
  19. Tumblin, J. and Turk, G. 1999. LCIS: A Boundary Hierarchy for Detail-Preserving Contrast Reduction.In Siggraph 1999, Computer Graphics Proceedings, pp. 83–90.Google Scholar
  20. Ward, G., Rushmeier, H., and Piatko, C. 1997. A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes. IEEE Trans. on Visualization and Computer Graphics,3(4).Google Scholar
  21. Wyszecki, G. and Stiles, W.S. 1982. Color Science: Concepts and Methods, Quantitative Data and Formulae (2nd ed.).New York: John Wiley & Sons, Inc.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Gloria Haro
    • 1
    Email author
  • Marcelo Bertalmío
    • 1
  • Vicent Caselles
    • 1
  1. 1.Departament de TecnologiaUniversitat Pompeu FabraBarcelonaSpain

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