Edge-preserving smoothing using median mean neural hybrid filters

Abstract A novel technique for edge-preserving smoothing is developed by using median mean neural hybrid filters. This filter structure is represented by the cascade connection of a median filter, a mean filter, and a neural network. This filter can adapt itself to the various noise environment through the learning of a training image. In the case where a priori data such a training image is unavailable, this filter can be efficiently applied to edge-preserving smoothing for the images degraded by the Gaussian and impulsive noises. Moreover, the structure of the proposed filter is very simple. Finally, the simulation examples show the effectiveness of the proposed filters.