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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193304" target="_blank" >RIV/00216305:26230/26:0193304 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-90065-5_28" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-90065-5_28</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-90065-5_28" target="_blank" >10.1007/978-3-031-90065-5_28</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-3-031-90064-8

ISSN

—

e-ISSN

—

Počet stran výsledku

16

Strana od-do

457-472

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Trieste

Místo konání akce

Terst

Datum konání akce

23. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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