Evolutionary Approximation in Non-Local Means Image Filters
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Valek:2022:SMC,
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author = "Matej Valek and Lukas Sekanina",
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booktitle = "2022 IEEE International Conference on Systems, Man,
and Cybernetics (SMC)",
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title = "Evolutionary Approximation in Non-Local Means Image
Filters",
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year = "2022",
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pages = "2762--2769",
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abstract = "The non-local means image filter is a non-trivial
denoising algorithm for color images using
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 using the standard mutation operator. Image
filters using evolved approximate multipliers can save
3percent in power consumption of multiplication
operations for a negligible drop in the image filtering
quality.",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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DOI = "doi:10.1109/SMC53654.2022.9945091",
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ISSN = "2577-1655",
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month = oct,
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notes = "Also known as \cite{9945091}",
- }
Genetic Programming entries for
Matej Valek
Lukas Sekanina
Citations