Abstract
This paper describes an interpolation algorithm for two-dimensional (2-D) discrete signals using fuzzy rule-based inference. The original signal is estimated by the main-surface function in the interpolation region, and four sub-plane functions surrounding the interpolation region. The main-surface is a bilinearly interpolated function passing through four signal samples in the interpolation region and the four sub-planes reflect the tendencies of pixels from the left, right, up, and down of the interpolation region. Drawing fuzzy inferences about signals from these five functions, we can estimate original signals very well even when the signals are buried in noise. We verified the method by computer simulations of some assumed 2-D signals and by resampling of the actual image data.
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