Copula Distribution Estimation Algorithm Based On Centroid And Its Application In Image Denoising

Es timation of distribut ion algorithm is a evolutionary algorithm of co mbining statistical learning.I t describes the evolution ten d of the entire group.Co p ula estimation of distribut ion alg o rithm is t h at copula theo ry is intr odu ced in to the estimatio n of distribut ion alg o rithm to simplify the operation of estimatin g the probability model.Copula estimation of distribution algorithm’s framew ork is simple,e asy to imp lement and easy to combine with other algorithms.But copula estimation of distribution algorithm use sampled individuals construct probabilistic models, the diversity o f the population decreas es.In t he late of ev oluti on,copula estimation of di stribut ion al gorithm’s local search capabi lity is weak.This paper mainly around that copula estimatio n of distribution algorithms ap plied to image denoising,and condu cts in-depth s tudy of improved copula esti mation of distribution algorithm,BP neural network based on copula esti mation of distrib ution algorithm and copula estimation of distribution algorithm in i mage den o ising.The main work of this paper are as follows:(1)Copula est ima tion of distri bution algorithm combined with the cen troi d calculation is that calcul atin g the cent roid o f optimum individuals of ea ch individual, than compare with this i ndivid u al to decide whether t o replace the individual. This strategy makes the individual species with better in formation search ing to the directio n of the opt imal solution and imp roves the local sear c h ab ility in ev o lutionary late. Throu g h thr ee standard test functions for testing and co mpar ed with UMDACG,MIMICCG,ES,cEDA.The results demonstrate the effectiven ess of the algorithm.(2)Exper ime ntal results show t h at the pro bability of the model structure,samp ling and cetroid calculation have a different role in the who le process of evolution.In the early evolu tio n,the play a lead ing role.In the late ev olution,centroid ca lculati o n to play a main role.Th erefore in order t o improve the operational efficiency of the al gorithm,this paper presents a dynamic r educe the sampling f requency.By reducin g t he comp lexi ty of the algorithm to improvethe algorithm’s running efficiency.(3)The weight and t h e thr eshold of BP networ k are seen as copula estimation of distr ibution algorithm’s optimization parameters.Copu la estimation of distr ibution optimize s BP neural network and g ets the bes t weighs and thresh old,so that the optimized BP neural net work can more accurately classified.By testing pattern clas sification datasets o f Iris and Wine in t h e stan d ard database UCI verif y the performanc e of the optimized BP neural network.(4)Due to the d rawbacks o f median filtering,this paper combin es copu la estimation o f dis tribution al g orithms with median filtering.First,the optimized BP neural network classifies t he pix els into noisy pixels and non-no isy pixels.Second,filtering the noi sy pi xels.The purpose is that removing noise while preserving image details.Experimental results show t hat this strateg y has higher peak signal to noise ratio and better denoising effect.