Abstract
The state of the art of current methods of film restoration (photographic and cinematographic) suggest the use of digital technologies to retrieve and restore faded and damaged original frames. In this context, the use of suitable mathematical and computational models can support the restorer in reconstructing the original state of the film and provide faster and cheaper restoration techniques. In this work, we discuss some of the main problems and open issues in film restoration, to promote new approaches and research directions that could benefit from mathematical modeling. In this direction, we present a color and contrast restoration approach based on the application of Spatial Color Algorithms (SCAs) to estimate the original color appearance in films, based on the Retinex model of human color visual sensation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
EU: European Broadcasting Union, Preservation and reuse of film material for television. Technical Report (2001)
Enticknap, L.: The Culture and Science of Audiovisual Heritage. Palgrave MacMillan, London (2013)
Fournier, V.: Digitisation opens up new prospects for audiovisual archives. Newspaper Tech. IV, 58–60 (2002)
Hauttekeete, L., Evens, T., De Moor, K., Schuurman, D.,Mannens, E., Van de Walle, R.: Archives in motion: Concrete steps towards the digital disclosure of audiovisual content. J. Cult. Herit. 12, 459–465 (2011)
Plutino, A., Lanaro, M.P., Liberini, S., Rizzi, A.: Work memories in Super 8: searching a frame quality metric for movie restoration assessment. J. Cult. Herit. 41, 238–248 (2020)
Plutino, A., Rizzi, A.: Research directions in color movie restoration. Color. Technol. 137, 78–82 (2021)
Van Dormolen, H.: Metamorfoze Preservation Imaging Guidelines. National programme for the preservation of paper heritage (2012)
FADGI: Technical Guidelines for Digitizing Cultural Heritage Materials. Federal Agencies Digital Guidelines Initiative, Still Image Working Group (2015)
Healey, G.E., Kondepudy, R.: Radiometric CCD camera calibration and noise estimation. IEEE Trans. Pattern Anal. Mach. Intell. 16, 267–276 (1994)
Tsin, Y., Ramesh, V., Kanade, T.: Statistical calibration of CCD imaging process. In: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 480–487. IEEE, Vancouver (2001)
McCann, J.J., Rizzi, A.: The Art and Science of HDR Imaging. John Wiley & Sons, New York (2011)
McCann, J.J., Vonikakis, V., Rizzi, A.: HDR Scene Capture and Appearance. SPIE Spotlight Series, San Francisco (2017)
Signoroni, A., Conte, M., Plutino, A., Rizzi, A.: Spatial–spectral evidence of glare influence on hyperspectral acquisitions. Sensors 20, 4374 (2020)
McCann, J.J., Rizzi, A.: Camera and visual veiling glare in HDR images. J. Soc. Inf. Display 15, 721–730 (2007)
Fossati, G.: From Grain to Pixel. Amsterdam University Press, Amsterdam (2009)
Gschwind, R., Frey, F.: Digital reconstruction of faded color photographs. Extrait de la Revue Informatique et Statistique dans les Sciences humaines XXXIII (1997)
Plutino, A., Crespi, A., Morabito, G., Sarti, B., Rizzi, A.: FiRe2: a call for a film repository of technical data and memories for photo and movie restoration. Cinergie - Il Cinema e le altre Arti 20, 69–83 (2021)
Barricelli, B.R., Casiraghi, E., Lecca, M., Plutino, A., Rizzi, A.: A cockpit of multiple measures for assessing film restoration quality. Pattern Recogn. Lett. 131, 178–184 (2020)
Jones, L.A., Condit, H.R.: The brightness scale of exterior scenes and the computation of correct photographic exposure. J. Opt. Soc. Am. 31, 651–678 (1941)
Vos, J.J., Van Den Berg, T.J.T.P.: Disability glare. CIE Res. Note 135/1 (1999)
Wright, W.D.: A plea to Edwin Land. Color. Res. Eng. 12, 119–120 (1987)
Rizzi, A.: What if colorimetry does not work. In: Proceedings of the IS & T International Symposium on Electronic Imaging: Color Imaging XXVI: Displaying, Processing, Hardcopy, and Applications 2021, pp. 323-1–323-6. Society for Imaging Science and Technology, Springfield (2021)
Rizzi, A.: Colour after colorimetry. Color. Technol. 137, 22–28 (2021)
Land, E.H.: The retinex theory of color vision. Sci. Am. 237, 108–129 (1977)
McCann, J.J., Parraman, C., Rizzi, A.: Reflectance, illumination, and appearance in color constancy. Front. Psychol. 5, 5 (2014)
Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Opt. Soc. Am. 61, 1–11 (1971)
McCann, J.J.: Retinex at 50: color theory and spatial algorithms, a review. J. Electron. Imaging 26, 031204 (2017)
McCann, J.J.: McCann Imaging. http://mccannimaging.com/Retinex
Rizzi, A., Bonanomi, C.: Milano Retinex family. J. Electron. Imaging 26, 031207 (2017)
Marini, D., Rizzi, A.: A computational approach to color adaptation effects. Image Vision Comput. 18, 1005–1014 (2000)
Provenzi, E., De Carli, L., Rizzi, A., Marini, D.: Mathematical definition and analysis of the Retinex algorithm. J. Opt. Soc. Am. A 22, 2613–2621 (2005)
Marini, D., Rizzi, A.: Colour constancy and optical illusions: a computer simulation with Retinex theory. In: 7th International Conference on Image Analysis and Processing (ICIAP93), pp. 657–660 (1993)
Simone, G., Audino, G., Farup, I., Albregtsen, F., Rizzi, A.: Termite Retinex: a new implementation based on a colony of intelligent agents. J. Electron. Imaging 23, 013006 (2014)
Provenzi, E., Fierro, M., Rizzi, A., De Carli, L., Gadia, D., Marini, D.: Random spray Retinex: a new Retinex implementation to investigate the local properties of the model. IEEE Trans. Image Process. 16, 162–171 (2006)
Banić, N., Lončarić, S.: Light random sprays Retinex: exploiting the noisy illumination estimation. IEEE Sig. Process. Lett. 20, 1240–1243 (2013)
Banić, N., Lončarić, S.: Smart light random memory sprays Retinex: a fast Retinex implementation for high-quality brightness adjustment and color correction. J. Opt. Soc. Am. A 32, 2136–2147 (2015)
Kolås, Ø., Farup, I., Rizzi, A.: Spatio-temporal Retinex-inspired envelope with stochastic sampling: a framework for spatial color algorithms. J. Imaging Sci. Technol. 55, 40503–1 (2011)
Gianini, G., Manenti, A., Rizzi, A.: Qbrix: a quantile-based approach to retinex. J. Opt. Soc. Am. A 31, 2663–2673 (2014)
Gatta, C., Rizzi, A., Marini, D.: Ace: An automatic color equalization algorithm. In: Conference on Colour in Graphics, Imaging, and Vision, pp. 316–320. Society for Imaging Science and Technology (2002)
Plutino, A., Barricelli, B.R., Casiraghi, E., Rizzi, A.: Scoping review on automatic color equalization algorithm. J. Electron. Imaging 30, 020901 (2021)
Frankle, J.A., McCann, J.J.: Method and apparatus for lightness imaging. Google Patents, US Patent 4,384,336 (1983)
McCann, J.J.: Lessons learned from mondrians applied to real images and color gamuts. In: Color and Imaging Conference, pp. 1–8. Society for Imaging Science and Technology (1999)
Pan, S., An, X., He, H.: Adapting iterative Retinex computation for high-dynamic-range tone mapping. J. Electron. Imaging 22, 023006 (2013)
Sobol, R.: Improving the Retinex algorithm for rendering wide dynamic range photographs. J. Electron. Imaging 13, 65–74 (2004)
Land, E.H.: An alternative technique for the computation of the designator in the retinex theory of color vision. Proc. Natl. Acad. Sci. 83, 3078–3080 (1986)
Funt, B., Ciurea, F., McCann, J.J.: Retinex in matlab. In: Color and Imaging Conference, pp. 112–121. Society for Imaging Science and Technology (2000)
Jobson, D.J., Rahman, Z., Woodell, G.A.: Retinex image processing: improved fidelity to direct visual observation. In: Color and Imaging Conference, pp. 124–125. Society for Imaging Science and Technology (1996)
Rahman, Z., Jobson, D.J., Woodell, G.A.: Multi-scale retinex for color image enhancement. In: Proceedings of 3rd IEEE International Conference on Image Processing, vol. 3, pp. 1003–1006 (1996)
Meylan, L., Susstrunk, S.: High dynamic range image rendering with a retinex-based adaptive filter. IEEE Trans. Image Process. 15, 2820–2830 (2006)
Saponara, S., Fanucci, L., Marsi, S., Ramponi, G., Kammler, D., Witte, E.M.: Application-specific instruction-set processor for retinex-like image and video processing. IEEE Trans. Circuits Syst. II: Express Briefs 54, 596–600 (2007)
Provenzi, E.: Computational Color Science: Variational Retinex-like Methods. John Wiley & Sons, New York (2017)
Caselles, V., Morel, J.-M., Sapiro, G., Tannenbaum, A.R.: Introduction to the special issue on partial differential equations and geometry-driven diffusion in image processing and analysis. IEEE Trans. Image Process. 7, 1058–1072 (1998)
Sapiro, G.: Geometric partial Differential Equations and Image Analysis. Cambridge University Press, Cambridge (2006)
Sapiro, G., Caselles, V.: Histogram modification via differential equations. J. Differ. Equ. 135, 238–268 (1997)
Bertalmío, M., Caselles, V., Provenzi, E., Rizzi, A.: Perceptual color correction through variational techniques. IEEE Trans. Image Process. 16, 1058–1072 (2007)
Hurlbert, A.: Formal connections between lightness algorithms. J. Opt. Soc. Am. A 3, 1684–1693 (1986)
Blake, A.: Boundary conditions for lightness computation in Mondrian world. Comput. Vision Graph. Image Process. 32, 314–327 (1985)
Morel, J.-M., Petro, A.B., Sbert, C.: A PDE formalization of Retinex theory. IEEE Trans. Image Process. 19, 2825–2837 (2010)
Limare, N., Petro, A.B., Sbert, C., Morel, J.-M.: Retinex Poisson equation: a model for color perception. Image Process. Line 1, 39–50 (2011)
Kimmel, R., Elad, M., Shaked, D., Keshet, R., Sobel, I.: A variational framework for retinex. Int. J. Comput. Vision 52, 7–23 (2003)
Gianini, G., Mio, C., Fossi, L.G., Rizzi, A.: Gradient attenuation as an emergent property of reset-based Retinex models. In: Proceedings of the 11th International Conference on Management of Digital EcoSystems, pp. 324–329 (2019)
Rizzi, A., McCann, J.J.: On the behavior of spatial models of color. In: Proceedings of SPIE and IS& T Electronic Imaging (2007)
Islam, A.T., Farup, I.: Spatio-temporal colour correction of strongly degraded movies. In: Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications, vol. 7866, pp. 278–292 (2011).
Rizzi, A., Berolo, A.J., Bonanomi, C., Gadia, D.: Unsupervised digital movie restoration with spatial models of color. Multimed. Tools Appl. 75, 3747–3765 (2016)
Machidon, O.-M., Ivanovici, M.: Digital color restoration for the preservation of reversal film heritage. J. Cult. Herit. 33 181–190 (2018)
Rizzi, A., Gatti, L., Kránicz, B., Berolo, A.J.: A mixed perceptual and physical-chemical approach for the restoration of faded positive films. In: Conference on Colour in Graphics, Imaging, and Vision, pp. 292–295. Society for Imaging Science and Technology (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Plutino, A., Sarti, B., Rizzi, A. (2023). Models and Mathematical Issues in Color Film Restorations. In: Bretti, G., Cavaterra, C., Solci, M., Spagnuolo, M. (eds) Mathematical Modeling in Cultural Heritage. MACH 2021. Springer INdAM Series, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-99-3679-3_13
Download citation
DOI: https://doi.org/10.1007/978-981-99-3679-3_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3678-6
Online ISBN: 978-981-99-3679-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)