Removal of impulse noise in digital images with naive Bayes classifier method

A new method has been presented in this paper to remove randomly formed impulse noise in digital images. This method is one of the favorite learning approaches of the Bayes learning method and is frequently called the na ve Bayes classifier. It has especially been used more frequently in recent times in the field of signal processing. Prior to restoration of the noisy pixels of the image as is done here, the image is first separated into pieces, and then an associated learning set is formed for each piece using the noise-free pixels. These learning sets that are different for each piece are used in order to estimate the pixel that will replace the noisy one. The proposed method is both simple and easy to apply. Our comprehensive experimental studies show that our proposed method outperforms other filters that are very popular in the literature.