Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

Year 2016, Special Issue (2016), 90 - 94, 01.12.2016

Süleyman Uzun Devrim Akgün

https://doi.org/10.18100/ijamec.266173

Abstract

One of the widespread image processing applications is image filtering
with two dimensional convolution. Determining the weights of image filters are
of importance for the success of filtering operation. Heuristic algorithms such
as genetic algorithms provide an efficient way of training these types of
filters. Due to the high computational cost of repetitive image filtering
operations, this process may take hours to implement using single core
computing. OpenMP (Open Multi Processing) provides an efficient library for
utilizing the computing power of multicore processors.  In this study, OpenMP accelerated training of
separable filters that are a subclass of convolution filters has been
implemented based on genetic algorithms. Comparative speed-up results for various
sizes of images using various sizes of filtering kernels were presented. Also
the effect of population size of genetic algorithm and the number of working
cores have been investigated.

Keywords

OpenMP, separable filters, image processing

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