Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Applied Intelligence
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
An implicit context representation for evolving image processing filters
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Graph-Based evolution of visual languages
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Generative art and evolutionary refinement
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
No photos harmed/growing paths from seed: an exhibition
NPAR '12 Proceedings of the Symposium on Non-Photorealistic Animation and Rendering
Generating knitting patterns from a sketch: a CSP approach
Proceedings of the Symposium on Computational Aesthetics
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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.