The Research On Image Processing Based On Type-2 Fuzzy Logic

Digital image processing has become an important method of acquiring complex information in an information age,and it has extended the vision of the human beings in some extent.Image denoising and edge-detection technology is an important part of the digital image processing.Image is affected by various factors in the process of imaging,there's a certain similarity and uncertainty in the process of image processing.The fuzzy set and fuzzy information processing technology has great advantages in processing with fuzzy uncertainty of events and the imprecise knowledge description and processing.This paper studies the fuzzy information processing technology based on image filtering and edge-detection algorithm,puts forward some new ideas.In this paper,we combine the uncertainty of image denoising and edge detectioninformation to study the related issues of image processing in the interval-2 type fuzzylogic algorithm.The main work and research results of the paper are as follows:It proposed an interval-2 type fuzzy neural network for image denoising.In each fuzzy rule of IT2 FNN,the first part uses the interval-2 type fuzzy set,and the subsequent part is the Mamdani type.Using interval type 2 fuzzy sets can help improve the network's noise immunity.IT2 FNN learning includes structural learning and parameter learning.For structural learning,an online clustering algorithm is proposed to automatically generate rules and flexibly assign it to the input space.For parameter learning,a regularly ordered Kalman filter algorithm is proposed to adjust some of the following parameters.Compared with the type-1 and type-2 fuzzy neural systems,the IT2FNN's learning ability and robustness are verified.The image impulse noise can be removed while the edges and important details of the image can be preserved completely.An edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic.The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection.For the defuzzification process,the heights and approximation methods are used.Simulation results with a type-1 fuzzy inference system,an interval type-2 fuzzy inference system,and with a generalized type-2 fuzzy inference system for edge detection are presented.The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images.The paper used the merit of Pratt measure to illustrate the advantages of using generalize d type-2 fuzzy logic.