Evolutionary Approximation in Non-Local Means Image Filters

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Valek:2022:SMC,

• author = "Matej Valek and Lukas Sekanina",
• booktitle = "2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
• title = "Evolutionary Approximation in Non-Local Means Image Filters",
• year = "2022",
• pages = "2762--2769",
• 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.",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
• DOI = "doi:10.1109/SMC53654.2022.9945091",
• ISSN = "2577-1655",
• month = oct,
• notes = "Also known as \cite{9945091}",

}

Genetic Programming entries for Matej Valek Lukas Sekanina

Citations