Complex-fuzzy adaptive image restoration: an artificial-bee-colony-based learning approach

Authors:
Chunshien Li;Fengtse Chan
Affiliations:
Department of Information Management, Nantional Central University, Taiwan, ROC;Department of Information Management, Nantional Central University, Taiwan, ROC
Venue:
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Year:
2011

Citing 13
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks
Fuzzy complex numbers

Fuzzy Sets and Systems
Fuzzy complex analysis I: differentiation

Fuzzy Sets and Systems
Fuzzy complex analysis II: integration

Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
The weighted median filter

Communications of the ACM
Digital Picture Processing

Digital Picture Processing
Inferring operating rules for reservoir operations using fuzzy regression and ANFIS

Fuzzy Sets and Systems
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Complex fuzzy sets

IEEE Transactions on Fuzzy Systems
Complex fuzzy logic

IEEE Transactions on Fuzzy Systems
Toward complex fuzzy logic

IEEE Transactions on Fuzzy Systems

A granular neural network: Performance analysis and application to re-granulation

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

A complex-fuzzy approach using complex fuzzy sets is proposed in the paper to deal with the problem of adaptive image noise cancelling. A image may be corrupted by noise, resulting in the degradation of valuable image information. Complex fuzzy set (CFS) is in contrast with traditional fuzzy set in membership description. A CFS has the membership state within the complexvalued unit disc of the complex plane. Based on the membership property of CFS, we design a complex neural fuzzy system (CNFS), so that the functional mapping ability by the CNFS can be augmented. A hybrid learning method is devised for training of the proposed CNFS, including the artificial bee colony (ABC) method and the recursive least square estimator (RLSE) algorithm. Two cases for image restoration are used to test the proposed approach. Experimental results are shown with good restoration quality.