Evolution of Cellular Automata with Conditionally Matching Rules for Image Filtering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138610" target="_blank" >RIV/00216305:26230/20:PU138610 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9185767" target="_blank" >https://ieeexplore.ieee.org/document/9185767</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC48606.2020.9185767" target="_blank" >10.1109/CEC48606.2020.9185767</a>

Result language

angličtina

Original language name

Evolution of Cellular Automata with Conditionally Matching Rules for Image Filtering

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2020 IEEE Congress on Evolutionary Computation (CEC)

ISBN

978-1-7281-6929-3

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

1-8

Publisher name

IEEE Computational Intelligence Society

Place of publication

Los Alamitos

Event location

Glasgow

Event date

Jul 19, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000703998202026