An impulse noise filtering algorithm based on a robust neuro-fuzzy network
Shitong Wang · Journal of Shandong University · 2010
Based on an integration of a simple impulse detector and a robust neuro-fuzzy(RNF)network,an effective impulse noise filtering algorithm for color images was presented.It consists of two modes of operation,namely,training and testing(filtering).During training,the impulse detector was used to locate the noisy pixels in the color images for optimizing the RNF network.During testing,if a pixel was detected as a corrupted one according to the impulse detector,the trained RNF network would be triggered to output a new pixel to replace it.The proposed impulse noise filtering algorithm was distinguished by two properties.The first step is the use of a simple impulse noise detector,which is efficient and yet effective in detecting the noise pixels in color images,and the second is the use of a novel membership function in the design of the adaptive RNF network,making the network robust to impulse noise.As demonstrated by the experimental results,the proposed filter not only had the abilities of noise attenuation and details preservation but also possessed the desirable robustness and adaptive capabilities.It outperforms other conventional multichannel filers.