A new method for image processing using generalized linguistic neutrosophic cubic aggregation operator

Abstract Image processing is a technique of scrutinizing an image and extricating important information. Indecisive situations are generally undergone when the picture processes with profuse noise. Neutrosophic set (NS), part neutrosophy theory, studies scope neutralities essential to reasoning incomplete uncertain However, linguistic neutrosophic cubic (LNCS) one extensions NS. In LNCS, each element characterized by interval-valued single-valued numbers handle data uncertainties. Keeping these features in mind, we apply LNCS for after defining their aggregation operators operations. this study, noisy grey-scale images were transformed into domain using three membership degrees, then aggregated operators. The proposed method clarifies noise Lena other test images. It has justified utilization based on visual clarity obtained. Suitable comparison analysis efficiency testing performed theory considering types, such as Gaussian, Poisson, Speckle. addition, have also compared computational our existing ones. results show that approach consumes less memory executes quicker than methods. A decision-maker can select more effective operator segment effectively obtained results.