Impulse noise removal by k-means clustering identified fuzzy filter: a new approach

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
Removal of impulse noise from corrupted digital images has been a hitch in the field of image processing. 4 Random nature of impulse noise makes the task of noise removal more critical. Different filters have been designed 5 for noise removal purpose and have shown formidable results mostly for low and medium level noise densities. In this 6 paper, a new two-stage technique called K-Means Clustering Identified Fuzzy Filter (KMCIFF) is proposed for de-noising 7 gray-scale images. KMCIFF consists
more » ... of a K-Means clustering-based high density impulse noise detection, followed by a 8 fuzzy logic-oriented noise removal mechanism. In the detection process, a 5 × 5 window centering upon each pixel of the 9 image is considered. K-Means clustering is applied on each 5 × 5 window to group the pixels into different clusters to 10 detect whether the central pixel of each window is noisy or not. In the noise removal process, a 7 × 7 window centering 11 upon each noisy pixel of the image, as detected by the clustering is considered. Fuzzy logic is used to find the non-noisy 12 pixel in each 7 × 7 window having the highest influence on the central noisy pixel of the window. Finally, that pixel 13 is replaced by the approximated pixel intensity value calculated from the highest influencing non-noisy pixel. KMCIFF 14 is evaluated upon seven different standard test images using Peak Signal to Noise Ratio (PSNR), Structural Similarity 15 Index Measurement (SSIM), Percentage of actual non-noisy pixels Detected As Erroneous out of the total number of 16 pixels (PDAE) and Average Run Time (ART). It has been observed that KMCIFF shows significantly more competitive 17 visual and quantitative performances vis-a-vis most of the extant traditional filters at high noise densities of up to 90 % . 18
doi:10.3906/elk-1910-34 fatcat:iwnrueg2ovh65glferqkh273pi