Intelligent fuzzy image filter for impulse noise removal

This paper proposes an intelligent fuzzy image filter (FIF) to remove impulse noise. The filter includes two processes, the intelligent fuzzy number deciding (IFND) process and fuzzy inference process, to filter impulse noise from heavily corrupted images efficiently. IFND can automatically decide the number of fuzzy number based on image features to overcome the drawbacks of adaptive weighted fuzzy mean (AWFM) filter that must be defined by domain expert. Moreover, the fuzzy inference process refers the knowledge base produced by IFND and fuzzy rule base that can improve the weakness of conventional filters in heavily corrupted condition. The intelligent FIF achieves better performance than the other filters based on the criteria of mean absolute error (MAE), and mean square error (MSE). By the experiments, FIF still keeps the high performance to filtering impulse noise from color image