Abstract:

 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 is presented. It consists of two modes of operation, namely, training and testing (filtering). During training, the impulse detector is used to locate the noisy pixels in the color images for optimizing the RNF network. During testing, if a pixel is detected as a corrupted one according to the impulse detector, the trained RNF network will be triggered to output a new pixel to replace it. The proposed impulse noise filtering algorithm is distinguished by two properties. The first is the use of a simple impulse noise detector, which is efficient and yet effective in detecting the noise pixels in color images. The other 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 has the abilities of noise attenuation and details preservation but also possesses desirable robustness and adaptive capabilities. It outperforms other conventional multichannel filers.

Key words:  color image processing, impulse noise, multichannel filter, neruo-fuzzy network, robust