Evolving Approximate Image Filters

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Colton, Simon; Torres, Pedro: Evolving Approximate Image Filters. In: EvoMUSART 2009, S. 467-477.




Image filtering involves taking a digital image and producing a new image from it. In software packages such as Adobe’s Photoshop, image filters are used to produce artistic versions of original images. Such software usually includes hundreds of different image filtering algorithms, each with many fine-tuneable parameters. While this freedom of exploration may be liberating to artists and designers, it can be daunting for less experienced users. Photoshop provides image filter browsing technology, but does not yet enable the construction of a filter which produces a reasonable approximation of a given filtered image from a given original image. We investigate here whether it is possible to automatically evolve an image filter to approximate a target filter, given only an original image and a filtered version of the original. We describe a tree based representation for filters, the fitness functions and search techniques we employed, and we present the results of experimentation with various search setups. We demonstrate the feasibility of evolving image filters and suggest new ways to improve the process.

Extended Abstract


booktitle={Applications of Evolutionary Computing},
series={Lecture Notes in Computer Science},
editor={Giacobini, Mario and Brabazon, Anthony and Cagnoni, Stefano and Di Caro, GianniA. and Ekárt, Anikó and Esparcia-Alcázar, AnnaIsabel and Farooq, Muddassar and Fink, Andreas and Machado, Penousal},
title={Evolving Approximate Image Filters},
url={http://dx.doi.org/10.1007/978-3-642-01129-0_53 http://de.evo-art.org/index.php?title=Evolving_Approximate_Image_Filters }, 
publisher={Springer Berlin Heidelberg},
author={Colton, Simon and Torres, Pedro},

Used References

Behrenbruch, C., Petroudi, S., Bond, S., Declerck, J., Leong, F., Brady, J.: Image filtering techniques for medical image post-processing: an overview. British Journal of Radiology 77(2)

Harding, S.: Evolution of image filters on graphics processor units using Cartesian genetic programming. In: IEEE Congress on Evolutionary Computation (2008)

Machado, P., Cardoso, A.: All the truth about NEvAr. App. Int. 16, 101–118 (2002)

Machado, P., Dias, A., Duarte, N., Cardoso, A.: Giving Colour to Images. In: Proceedings of the AISB 2002, Symposium on Artificial Intelligence and Creativity in Arts and Science (2002)

Poli, R., Langdon, W.: Schema Theory for Genetic Programming with One-Point Crossover and Point Mutation. Evolutionary Computation 6(3), 231–252 (1998)

Sekanina, L., Martínek, T.: Evolving image operators directly in hardware. In: Proc. of Genetic and Evol. Computation for Image Processing and Analysis (2007)

Sims, K.: Artificial evolution for computer graphics. In: Proceedings of SIGGRAPH 1991 (1991)

Smith, S., Leggett, S., Tyrrell, A.: An implicit context representation for evolving image processing filters. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 407–416. Springer, Heidelberg (2005)

Torres, P., Colton, S., Rüger, S.: Experiments in example-based image filter retrieval. In: Proceedings of the Cross-Media Workshop (2008)


Full Text


intern file

Sonstige Links