Abstract
Film grain is a highly valued characteristic of analog images, thus realistic digital film grain synthesis is an important objective for many modern photographers and film-makers. We carry out a theoretical analysis of a physically realistic film grain model, based on a Boolean model, and derive expressions for the expected value and covariance of the film grain texture. We approximate these quantities using a Monte Carlo simulation, and use them to propose a film grain synthesis algorithm based on Gaussian textures. With numerical and visual experiments, we demonstrate the correctness and subjective qualities of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Please note that the Boolean model is in fact defined in a much more general fashion, but for our purposes this definition is sufficient.
References
Bae, S., Paris, S., Durand, F.: Two-scale tone management for photographic look. In: ACM Transactions on Graphics, vol. 25 (2006)
Bayer, B.E.: Relation between granularity and density for a random-dot model. J. Opt. Soc. Am. 54(12), 1485+ (1964)
Chiu, S.N., Stoyan, D., Kendall, W.S., Mecke, J.: Stochastic Geometry and Its Applications, 3rd edn. Wiley, Hoboken (2013)
DxO: Dxo film pack 5 (2016). http://www.dxo.com/us/photography/photo-software/dxo-filmpack
Galerne, B., Gousseau, Y., Morel, J.M.: Random phase textures: theory and synthesis. IEEE Trans. Image Process. 20(1), 257–267 (2011)
G’MIC: Greyc’s magic for image computing (2016). http://gmic.eu/
Grubbasoftware: Truegrain (2015). http://grubbasoftware.com/
Hoeffding, W.: The strong law of large numbers for u-statistics. Institute of Statistics mimeo series 302 (1961)
Livingston, R.: The theory of the photographic process. By C. E. Kenneth Mees. J. Phys. Chem. 49(5), 509 (1945)
Moldovan, T.M., Roth, S., Black, M.J.: Denoising archival films using a learned bayesian model. In: 2006 IEEE International Conference on Image Processing, pp. 2641–2644. IEEE, October 2006
Newson, A., Galerne, B., Delon, J.: Stochastic modelling and realistic rendering of film grain. Technical report, Laboratoire MAP5, Université Paris Descartes, France (2016)
Oh, B.T., Lei, S.M., Kuo, C.C.: Advanced film grain noise extraction and synthesis for high-definition video coding. IEEE Trans. Circ. Syst. Video Technol. 19(12), 1717–1729 (2009)
Schneider, R., Weil, W.: Stochastic and Integral Geometry. Springer, Heidelberg (2008)
Tanaka, K., Uchida, S.: Extended random-dot model. J. Opt. Soc. Am. 73(10), 1312+ (1983)
The CGAL Project: CGAL User and Reference Manual. 4.9 edn. (2016). http://doc.cgal.org/4.9/Manual/packages.html
Yan, J.C.K.: Statistical methods for film grain noise removal and generation. Master’s thesis, University of Toronto (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Newson, A., Faraj, N., Delon, J., Galerne, B. (2017). Analysis of a Physically Realistic Film Grain Model, and a Gaussian Film Grain Synthesis Algorithm. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-58771-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58770-7
Online ISBN: 978-3-319-58771-4
eBook Packages: Computer ScienceComputer Science (R0)