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

2011 ◽  
Author(s):  
Daoyuan Zhai ◽  
Minshen Hao ◽  
Jerry M. Mendel
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Noise Removal ◽  
Fuzzy Logic System ◽  
Image Noise ◽  
Swarm Optimization ◽  
Interval Type ◽  
Logic System

UNIVERSAL IMAGE NOISE REMOVAL FILTER BASED ON TYPE-2 FUZZY LOGIC SYSTEM AND QPSO

2012 ◽  
Vol 20 (supp02) ◽  
pp. 207-232 ◽  
Author(s):  
DAOYUAN ZHAI ◽  
MINSHEN HAO ◽  
JERRY MENDEL
Keyword(s):  
Fuzzy Logic ◽  
Impulse Noise ◽  
Integrated Approach ◽  
Noise Removal ◽  
Fuzzy Logic System ◽  
Block Matching ◽  
Discrete Cosine Transformation ◽  
Interval Type ◽  
Logic System

Removing Mixed Gaussian and Impulse Noise (MGIN) is considered to be one of the most essential topics in the domain of image restoration, and it is much more challenging than to remove pure Gaussian or impulse noise separately. Therefore, relatively fewer works have been published in this area. This paper proposes a new integrated approach for MGIN removal that is based on a Non-Singleton Interval Type-2 (NS-IT2) Fuzzy Logic System (FLS), and explains how to design such a NS-IT2 FLS using a Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. Then the paper goes on to introduce two supplementary components, a Block-Matching 3-Dimensional Discrete Cosine Transformation (BM3D DCT) filter and a contrast scaling filter, which augment the overall performance of the NS-IT2 FLS. Finally, the paper shows that this proposed approach indeed provides both quantitatively and visually much better results compared to other often-used non-fuzzy techniques as well as its Type-1 (T1) and singleton IT2 (S-IT2) counterparts.

Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic

2014 ◽  
Vol 20 (3) ◽  
pp. 1057-1070 ◽  
Author(s):  
Frumen Olivas ◽  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Dynamic Parameter ◽  
Swarm Optimization ◽  
Parameter Adaptation ◽  
Interval Type
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