Adaptive image restoration by a novel neuro-fuzzy approach using complex fuzzy sets

Authors:
Chunshien Li
Affiliations:
Laboratory of Intelligent Systems and Applications, Department of Information Management, National Central University, No. 300, Jung-da Rd., Jung-li City, Taoyuan, Taiwan
Venue:
International Journal of Intelligent Information and Database Systems
Year:
2013

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Abstract

A complex neuro-fuzzy approach using new concept of complex fuzzy sets and neuro-fuzzy system is presented to deal with the problem of adaptive image noise cancelling AINC. An image can be tainted by unknown noise, resulting in the degradation of valuable image information. A complex fuzzy set CFS is characterised in the unit disc of the complex plane by a complex-valued membership function that includes an amplitude function and a phase function. Based on the nature of CFSs, several CFSs can be used to design a complex neural fuzzy system CNFS for the application of AINC. To train the CNFS, a hybrid learning method is used, where the algorithm of artificial bee colony ABC and the method of recursive least squares estimator RLSE are integrated in a complementarily hybrid way. Three cases are used to test the proposed CNFS for image restoration. The experimental results by the proposed CNFS approach are compared with those by other approaches and the proposed approach has shown promising performance.