Evolutionary Approximation in Non-Local Means Image Filters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU145930" target="_blank" >RIV/00216305:26230/22:PU145930 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SMC53654.2022.9945091" target="_blank" >http://dx.doi.org/10.1109/SMC53654.2022.9945091</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC53654.2022.9945091" target="_blank" >10.1109/SMC53654.2022.9945091</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary Approximation in Non-Local Means Image Filters

Popis výsledku v původním jazyce

The non-local means image filter is a non-trivial denoising algorithm for color images utilizing floating-point arithmetic operations in its reference software implementation. In order to simplify this algorithm for an on-chip implementation, we investigate the impact of various number representations and approximate arithmetic operators on the quality of image filtering. We employ Cartesian Genetic Programming (CGP) to evolve approximate implementations of a 20-bit signed multiplier which is then applied in the image filter instead of the conventional 32-bit floating-point multiplier. In addition to using several techniques that reduce the huge design cost, we propose a new mutation operator for CGP to improve the search quality and obtain better approximate multipliers than with CGP utilizing the standard mutation operator. Image filters utilizing evolved approximate multipliers can save 35% in power consumption of multiplication operations for a negligible drop in the image filtering quality.

Název v anglickém jazyce

Evolutionary Approximation in Non-Local Means Image Filters

Popis výsledku anglicky

The non-local means image filter is a non-trivial denoising algorithm for color images utilizing floating-point arithmetic operations in its reference software implementation. In order to simplify this algorithm for an on-chip implementation, we investigate the impact of various number representations and approximate arithmetic operators on the quality of image filtering. We employ Cartesian Genetic Programming (CGP) to evolve approximate implementations of a 20-bit signed multiplier which is then applied in the image filter instead of the conventional 32-bit floating-point multiplier. In addition to using several techniques that reduce the huge design cost, we propose a new mutation operator for CGP to improve the search quality and obtain better approximate multipliers than with CGP utilizing the standard mutation operator. Image filters utilizing evolved approximate multipliers can save 35% in power consumption of multiplication operations for a negligible drop in the image filtering quality.

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

<a href="/cs/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Aproximativní neurovýpočty</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

ISBN

978-1-6654-5258-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

2759-2766

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

9. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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