Douglas J. Lockett (EE) and Christopher D. Roblee (CpE)
Advisor: Professor Michael Rudko
In today’s fast-paced society, effective real-time processing
technology is expected and therefore arguably essential. The rapid growth
and prevalence of digital multimedia has driven the development of
associated technologies. Modern signal and image processing applications
demand significant levels of computation, many of which are too complex
for practical implementation using software alone. This paper outlines the
use of a genetic algorithm to design multiplierless recursive IIR filters
for applications in hardware-based image processing. A unique genetic
algorithm was developed to optimize filter coefficients such that the
corresponding filter’s frequency response matches that of an ideal system
with the constraint that all coefficients are powers-of-two and the
resulting filter is stable. The motivation for using power-of-two filter
coefficients is to reduce the overall arithmetic complexity in any
hardware based implementation by replacing digital multipliers with
simpler shift operators. This approach is highly beneficial for image
filtering applications that are computationally intensive. The cases
considered comprise Canny’s edge detection filter as well as an image blur
operator. The resulting multiplierless filters are compared to analogous
implementations using real multipliers on the basis of complexity (the
number of shifts and additions performed), frequency response, and
qualitative performance on test images. It is shown that in many cases the
multiplierless systems have a definite advantage in terms of their
efficiency while maintaining a desired response, making them a viable
alternative as image filters. It is demonstrated that custom genetic
optimization is a reliable, efficient, and in specific circumstances a
superior means for realizing such filters.
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