Publication result detail

Multi-purpose Image Filter Evolution Using Cellular Automata and Function-Based Conditional Rules

BIDLO, M.; SARANOVÁ, I.

Original Title

Multi-purpose Image Filter Evolution Using Cellular Automata and Function-Based Conditional Rules

English Title

Multi-purpose Image Filter Evolution Using Cellular Automata and Function-Based Conditional Rules

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

cellular automaton, image filter, evolutionary algorithm, conditionally matching rule

Key words in English

cellular automaton, image filter, evolutionary algorithm, conditionally matching rule

Authors

BIDLO, M.; SARANOVÁ, I.

Released

18.04.2025

Publisher

Springer Nature Switzerland AG

Location

Trieste

ISBN

978-3-031-90064-8

Book

Applications of Evolutionary Computation: 28th European Conference, EvoApplications 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23-25, 2025, Proceedings, Part II

Edition

Lecture Notes in Computer Science

Volume

15613

Pages from

457

Pages to

472

Pages count

16

URL

https://link.springer.com/chapter/10.1007/978-3-031-90065-5_28

BibTex

@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"
}