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Image processing is considered a good candidate for the application of parallel processing because of the large volumes of data and the complex algorithms commonly encountered. Research on the architectures of parallel image processing systems is the basis of system designing. It plays an important role in achieving optimal conversion from algorithm to structure so that it is helpful to design a highly efficient system. Parallel image processing systems can be classified into three categories: computer-based dedicated systems, computer-based general systems and DSP-based systems. According to this classification, analysis and comparison of many kinds of realization technologies and structure characteristics were carried out. We also gave some application examples. In particular, research on the two most generic parallel image processing architectures of DSP and Cluster was executed. Then we performed image restoration and super-resolution algorithms on each of them. The experiment results show that DSP is adapted to small images' fast processing and Cluster is good at processing vast volumes and large scale images on the contrary.