Implementing a real-time image-processing algorithm on a serial processor is difficult to achieve because such a processor cannot cope with the volume of data in the low-level operations. However, a parallel implementation, required to meet timing constraints for the low-level operations, results in low resource utilisation when implementing the high-level operations. These factors suggested a combination of parallel hardware, for the low-level operations, and a serial processor, for the high-level operations, for implementing a high-level image-processing algorithm. Several types of serial processors were available. A general-purpose processor requires an extensive instruction set to be able to execute any arbitrary algorithm resulting in a relatively complex instruction decoder and possibly extra FUs. An application-specific processor, which was considered in this research, implements enough FUs to execute a given algorithm and implements a simpler, and more efficient, instruction decoder. In addition, an algorithms behaviour on a processor could be represented in either hardware (i.e. hardwired logic), which limits the ability to modify the algorithm behaviour of a processor, or “software” (i.e. programmable logic), which enables external sources to specify the algorithm behaviour. This research investigated hardware- and software- controlled application-specific serial processors for the implementation of high-level image-processing algorithms and compared these against parallel hardware and general-purpose serial processors. It was found that application-specific processors are easily able to meet the timing constraints imposed by real-time high-level image processing. In addition, the software-controlled processors had additional flexibility, a performance penalty of 9.9% and 36.9% and inconclusive footprint savings (and costs) when compared to hardwarecontrolled processors.