Modified CPI filter algorithm for removing salt-and-pepper noise in digital images

Abstract

In this paper, the theoretical aspects, implementation issues, and performance analysis of a modified CPI filter algorithm are presented. As the concept of the original CPI algorithm is to identify corrupted pixels by interrogating subimages, and considering the intensity spread of pixel values within the subimage when making a decision, the modified algorithm similarly takes into account the subimage gray level distribution across the whole gray scale. It works on the assumption that to consider which group in the subimage is corrupted, the multiple- feature histogram representing a subimage gray level distribution must be transformed into a two-feature histogram such that these two features can be mapped onto the two available pixel classes. This transformation is performed by using a 1-sigma decision about the mean intensity of the subimage, which enables pixels that fall inside the sigma bounds to be considered as uncorrupted, and the rest corrupted. A performance analysis of the modified CPI, original CPI, average, median and sigma algorithms is given for noisy images corrupted by salt-and- pepper noise of the impulsive and Gaussian nature, and gray noise over the signal-to-noise ratios (SNR) of plus 50 dB to minus 50 dB. The results show that similar to the original CPI algorithm, the modified CPI algorithm exhibits a number of desirable features. Firstly, due to its pixel identification property, it has better noise removing capability than the conventional filter algorithms. Secondly, most features in the original image are preserved in the restored image compared with, say, the median filter. Thirdly, iterative filtering of a noisy image using the CPI algorithm is possible.