Mixed Image Filtering Based On Threshold Division

Different from the linear filter, the non-linear filter cannot only remove noise effectively but also keep the details of the digital images, so images can be clearer and more vivid. As an effective processing technology, the non-linear filter is widely used in the digital image processing. Median filters are representative. The typical median filter can remove the impulse noise, but it also corrupts some very important details of the images. There are many improved median filters firstly suggested to overcome the problems.To overcome the drawback of the standard median filter,the adaptive filter is proposed in this paper. In the algorithm, threshold division is utilized to determine possible noisy point,and the proper point with isolated characteristic is selected,then the noise found in images is removed by means of improved median filter. Results of computer simulation experiments on the adaptive median filter show that the method can suppress noise effectively while preserving fine details very well and has better filtering performance than the standard median filter. It also shows greater advantages over the standard one in filtering speed.However, there are many different noise in one image usually .The most common type are the impulse noise and Gauss noise. It will be not easy to remove mixed noise in a single filter. To remove mixed impulse noise and Gauss noise in digital images, a new mixed filter based on the adaptive median filter and fuzzy weighted mean filter is proposed. The algorithm first classifies the pixels into two classes,one is the pixels which are corrupted by Gauss noise and the other is the pixels corrupted by impulse noise and then fuzzy weighted mean filter is used for the pixels corrupted by Gauss noise and the adaptive median filter is used for the pixels corrupted by impulse noise.The simulation result verifies that the proposed method is feasible and efficient.