A Neighborhood Impact Driven K-Medoid Clustering and Fuzzy Logic Blended Approach for High Density Impulse Noise Detection and Removal

Abstract

In the field of image processing, removing impulse noise has been regarded as one of the most important tasks, primarily because of the noise pattern it presents. Existing filters used the effect of only those non-noisy pixels which were present inside the specified windows ignoring the effect of the non-noisy pixels present in the surrounding windows. So, the least distant non-noisy pixels in the present window as well as in the surrounding windows may have an influence on the present window's noisy pixels. Hence, considering the above factors, in this paper, a two-step technique named KMDCIFF (K-medoid clustering identified fuzzy filter) is proposed for removing impulse noise from digital images. In the proposed KMDCIFF algorithm, the first step is noise detection using K-medoid clustering, followed by a fuzzy logic-acquainted noise reduction strategy that utilizes the least distant local and non-local non-noisy pixels for removal operation. The detection process involves the application of K-medoid clustering on all 5×5 windows produced by centering each pixel of the considered image. In order to remove noise, a 7×7 window is constructed with each detected noisy pixel in the center. Analyzing the impact of the least distant local and non-local pixel on each noisy pixel, the same is replaced by an estimated pixel’s intensity value obtained from the most influential non-noisy pixels. KMDCIFF is evaluated using well-known metrics for diverse types of images. At a high noise density of 80%, KMDCIFF exhibited significant peak-signal-to-noise-ratios (PSNRs) of 26.97 dB and 29.67 dB and structural similarity indexes (SSIMs) of 0.8045 and 0.9288 on random and fixed valued impulse noise impacted Lena image, respectively. Comparing the results of the contemporary study to those of previous studies of a similar kind in this sector, the results are unswervingly astounding.