Edge-preserving smoothing using median finite impulse response (FIR) hybrid filters

In image processing, one of the important problems is edge-preserving smoothing in mixed noise environment such that both Gaussian noise and impulsive noise exist. Recently, several types of the hybrid filter which is a kind of nonlinear filter have been proposed for this purpose. In this paper, a technique for edge-preserving smoothing is developed by using median finite impulse response (FIR) neural hybrid filters. This filter structure is represented by the cascade connection of median filter, FIR filter, and neural network. In this structure, the section of a median filter selects the median value among 3 points and the section of an FIR filter calculates the mean value of 3 points. The section of neural network consists of three layered structure and its inputs equal the output from the section of a median filter and the output from the section of an FIR filter. The major features of this filter are as follows: (1) This filter can adapt itself to the various noise environment through the learning of a training image. (2) Even if a priori data such a training image is unavailable, this filter can efficiently be 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.