A Non-Singleton Interval Type-2 Fuzzy Logic System for universal image noise removal using Quantum-behaved Particle Swarm Optimization

Removing Mixed Gaussian and Impulse Noise (MGIN) is considered to be very important in the domain of image restoration, but it is a somewhat more challenging topic than removing pure Gaussian or impulse noise. Therefore, relatively fewer works have been published in this area. This paper pro poses a Non-Singleton Interval Type-2 (IT2) Fuzzy Logic System (FLS) for MGIN removal, explains how it can be designed based on a Quantum-behaved Particle Swarm Optimization algorithm, and shows that it provides both quantitatively and visually much better results compared to other often-used non-fuzzy techniques as well as its Type-1 and singleton IT2 counterparts.