Publication Details
Evolutionary Approximation in Non-Local Means Image Filters
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Cartesian genetic programming, image filter, approximate multiplier, automated
design, mutation
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.
@inproceedings{BUT179617,
author="Matěj {Válek} and Lukáš {Sekanina}",
title="Evolutionary Approximation in Non-Local Means Image Filters",
booktitle="2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
year="2022",
pages="2759--2766",
publisher="Institute of Electrical and Electronics Engineers",
address="Praha",
doi="10.1109/SMC53654.2022.9945091",
isbn="978-1-6654-5258-8"
}