Research On Multiresolution Hidden Markov Model For Image Denoising

Image denoising is the foundation of high level image processing,such as imagesegmentation,coding,pattern recognition,etc.The research for image denoising algorithm hasreceived extensive attention because the applications of these high level image processingtechniques are directly influnced by the denoising results.There are two different ways of imagedenoising: spatial domain algorithm and transform domain algorithm.The main methods in spatialdomain include mean filter,median filter,wiener filter,and Markov Random Field Model.Allthese methods are mainly based on the similarity of neighboring pixcels.The methods intransform domain first transform the image using Fourier transform,wavelet transform and filterbanks decomposition,then images are denoised by.adjusting these coefficients.The researchs forimage denoising are mainly concerntrated on wavelet transform and filter bank decomposition inresent years,because the coefficients have the characteristics of multiresolution analysis and highorder decorrelation,.The main work and contribution in this thesis include:The wavelet coefficients have a non-gaussian symmetric marginal distribution,and haveproperties of intra-scale clustering and inter-scale persistence.So it is the foundation for theconstruction of statistical image processing models to analysis the marginal and conditionaldistribution of wavelet coefficients.In traditional Hidden Markov Tree model,the clusteringproperty of coefficients is less take into account.In this thesis,based on the qualitative analysis ofthe marginal and conditional distribution of wavelet coefficients,and using random field that isbased on the fuzzy logic,the clustering constraints are take account into the training process ofhidden markov tree model to efficiently enhance the precision of parameter estimation.Non seperable directional filter bank can decompose an image into multiresolution structureand can efficiently extract directional information.In this thesis,using histogram technique,aqualitative analysis of the marginal and conditional distribution is implemented.Then weconstructed a hidden markov tree model for the coefficients,and used this model for imagedenoising.Compared with the denoising results based on DB4 wavelet transform,it can be seenthat the model based on directional filter bank has very excellent denoising performance,especially for the images having rich textual information.Compared with 2-band wavelet transform,M-band wavelet transform can partition thefrequency domain with finer structure and its interscale coefficients have a structure ofhexadecimal tree.Currentely,the M-Band based image denoising algorithms are mainlythreadsholding methods,Through qualitative analysis of conditional and joint distribution of thecoefficients,it can be seen that multiscale random field model is suitable for the modeling of thecoefficients.Traditional markov random field model has an isotropic structure and is not suitablefor the correlation structure of the M-band coefficients.The estimation of field model parametersis also time consuming and need a large amount of memory.In the reference of the random fieldmodel based on fuzzy logic,in this thesis,a spatially adatptive multiscale hidden markov randomfield is constructed for the M-band coefficients.In the model,the likelihood function is computedusing psudo likelihood and the ML-MAP algorithm is used for parameter estimation.Experimentsshow that compared with the performence of threadtholding algorithms,the denoising results is very excellent,especially in high level noise situation.