Noise Reduction in Grayscale Image Using Segmentation Techniques

Clustering algorithm is widely used Segmentation method in image processing. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. The occurrence of noise during image acquisition, this might affect the processing result. Denoise based clustering algorithm has three variations namely, denoise based K-means, denoise based Fuzzy C-means, and denoise based Moving K-means. Denoise based clustering algorithm to minimize the salt and pepper noise and improve the image quality. The proposed DB-clustering algorithms are able to minimize the effects of the Salt-and-Pepper noise during the segmentation process without degrading the fine details of the images. The result obtained PSNR and SNR have favored the proposed denoise clustering algorithms, which consistently outperform the conventional clustering algorithms in segmenting the noisy images.