Result Details
Multi-purpose Image Filter Evolution Using Cellular Automata and Function-Based Conditional Rules
Saranová Ivana, Ing.
A variant of Evolution Strategy is applied to design transition functions for
cellular automata using a newly proposed representation denominated as
function-based conditional rules. The goal is to train the cellular automata to
eliminate various types of noise from digital images using a single evolved
function. The proposed method allowed us to design high-quality filters working
with 5-pixel neighbourhood only which is substantially more efficient than 9 or
even 25 pixels used by most of the existing filters. We show that salt-and-pepper
noise and random noise of several tens of percentages intensity may successfully
be treated. Moreover, the resulting filters have also shown an ability to filter
impulse-burst noise for which they were not trained explicitly. Finally we
demonstrate that our filters are capable to tackle with up to 40\% random noise
where most of existing filters fail.
cellular automaton, image filter, evolutionary algorithm, conditionally matching
rule
@inproceedings{BUT193304,
author="Michal {Bidlo} and Ivana {Saranová}",
title="Multi-purpose Image Filter Evolution Using Cellular Automata and Function-Based Conditional Rules",
booktitle="Applications of Evolutionary Computation: 28th European Conference, EvoApplications 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23-25, 2025, Proceedings, Part II",
year="2025",
series="Lecture Notes in Computer Science",
volume="15613",
pages="457--472",
publisher="Springer Nature Switzerland AG",
address="Trieste",
doi="10.1007/978-3-031-90065-5\{_}28",
isbn="978-3-031-90064-8",
url="https://link.springer.com/chapter/10.1007/978-3-031-90065-5_28"
}