Noise Tainted RGB ImageThresholding by Integrating SGO and Kapur’s Function

Image thresholding is an extensively accepted segmentation practice to extract the section of attention from a digital picture. Here, multi-thresholding is projected for the RGB picture with Social Group Optimization (SGO) algorithm. The chief motivation of this work is to investigate the presentation of well-known image segmentation procedure known as Kapur’s function. SGO and Kapur integrated procedures considered to enhance RGB picture stained with noises, like Gaussian (GN) and Speckle (SN). The capability of Kapur’s function is established with the well-known image quality measures available. The simulation outcome authenticates that, for the considered problem, Kapur’s offers better result for the original and noise stained images.