Spatially adaptive interpolation of digital images using fuzzy inference

This paper presents a novel adaptive interpolation method for digital images. This new method can reduce dramatically the blurring and jaggedness artifacts on the high-contrast edges, which are generally found in the interpolated images using conventional methods. This high performance is achieved via two proposed operators: a fuzzy-inference based edge preserving interpolator and an edge-shifted matching scheme. The former synthesizes the interpolated pixel to match the image local characteristics. Hence, edge integrity can be retained. However, due to its small footage, it does not work well on the sharply curved edges that have very sharp angles against one of the coordinates. Therefore, the edge-shifted matching technique is developed to identify precisely the orientation of sharply curved edges. By combining these two techniques, the subjective quality of the interpolated images is significantly improved, particularly along the high-contrast edges. Both synthesized images (such as letters) and natural scenes have been tested with very promising results.