The domain of image processing technologies comprises many methods and algorithms for the analysis of signals, representing data sets, as photos or videos. In this paper we present a discussion and analysis, on the one hand, of classical image processing methods, as Fourier transformation, and, on the other hand, of neural networks. Specifically we focus on multi-layer and convolutional neural networks and give guidelines how images can be analyzed effectively and efficiently. To speed up the performance we identify various parallel software and hardware environments and evaluate, how parallelism can be used to improve performance of neural network operations. Based on our findings we derive several guidelines for applying different parallelization approaches on various sequential and parallel hardware infrastructure.

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