This paper examines the application of extended permutation rank selection (EPRS) filters in image interpolation. EPRS filters are constrained to output an order statistic based on N observation samples and K statistics that are functions of the observation samples. By including the sample mean as the sole statistic when K=1, EPRS filters were shown to have superior edge-enhancing properties. By letting K=0, we can define a subset class known as rank conditioned rank selection (RCRS) filters. We show that these filters can also be applied to the issue of image interpolation. In this case, by extending the observation space by including more original samples and by adding additional linear statistics, EPRS filters produce results superior to traditional methods in the application of image interpolation.
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