Evolutionary Approximation in Non-Local Means Image Filters
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Evolutionary Approximation in Non-Local Means Image Filters
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
ISBN
978-1-6654-5258-8
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
2759-2766
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
Praha
Event location
Praha
Event date
Oct 9, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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