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Evolving Approximate Image Filters

  • Simon Colton
  • Pedro Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)

Abstract

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.

Keywords

Original Image Random Generation Colour Histogram Image Filter Colourful Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Simon Colton
    • 1
  • Pedro Torres
    • 1
  1. 1.Computational Creativity Group Department of ComputingImperial College LondonUK

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