Publication Details
Evolution of Cellular Automata with Conditionally Matching Rules for Image Filtering
evolution strategy, cellular automaton, conditional rule, image filter,
salt-and-pepper noise
We present an evolutionary method for the design of image filters using
two-dimensional uniform cellular automata. Specifically, a technique called
Conditionally Matching Rules is applied to represent transition functions for
cellular automata working with 256 cell states. This approach allows reducing the
length of chromosomes for the evolution substantially which was a need for such
high number of states since the traditional table based encoding would require
enormous memory space. The problem of removing Salt-and-Pepper noise from 8-bit
grayscale images is considered as a case study. A cellular automaton will be
initialised by the values of pixels of a corrupted image and a variant of
Evolution Strategy will be applied for the design of a suitable transition
function that is able to eliminate the noise from the image during ordinary
development of the cellular automaton. We show that using only 5-cell
neighbourhood of the cellular automaton in combination with conditionally
matching rules the resulting filters are able to provide a very good output
quality and are comparable with several existing solutions that require more
resources. Moreover, the proposed evolutionary method exhibits a high performance
which allows us to design filters in very short time even on a common PC.
@inproceedings{BUT168119,
author="Michal {Bidlo}",
title="Evolution of Cellular Automata with Conditionally Matching Rules for Image Filtering",
booktitle="2020 IEEE Congress on Evolutionary Computation (CEC)",
year="2020",
pages="1--8",
publisher="IEEE Computational Intelligence Society",
address="Los Alamitos",
doi="10.1109/CEC48606.2020.9185767",
isbn="978-1-7281-6929-3",
url="https://ieeexplore.ieee.org/document/9185767"
}