Research On Nonparametric Bayesian Dictionary Learning Methods In Sparse Gradient Domain For Image Denoising

In human daily life,image plays an increasingly important role.Image contains abundant information,but image is affected by various factors,resulting in poor quality.Generally,the image is polluted by noise,which directly affects the visual effect of the image.In order to solve this problem,image denoising technology has been developed maturely.The main purpose is not only to effectively remove image noise,but also to effectively retain useful information of the image,including texture and edge information of the image.In recent years,with the continuous improvement of signal sparse representation theory,the dictionary learning method based on sparse representation theory has been developed maturely.Considering that the original image is sparse in a proper dictionary and the noise usually does not have this characteristic,researchers have put forward many effective solutions to solve the problem of image denoising.The image denoising method based on sparse representation is difficult to determine the variance of image noise,and the traditional dictionary learning method is difficult to solve the problem of automatic parameter selection.The nonparametric Bayesian dictionary learning method can effectively solve this problem.Considering that the image has good sparsity and nonlocal self-similarity in gradient domain,the nonparametric Bayesian dictionary learning image denoising method in sparse gradient domain and the patch grouping Bayesian learning image denoising method in gradient domain are proposed respectively.The sparsity of image in gradient domain is generally better than that in spatial domain.A nonparametric Bayesian dictionary learning method for image denoising in sparse gradient domain is proposed.Considering that the whole image denoising model is a multi-variable coupling problem,it is difficult to solve directly.Bregman method and alternating iteration method are used to decompose the problem into several sub-problems,and then the least squares method and nonparametric Bayesian dictionary learning BPFA(Beta Process Factor Analysis)method are used to solve these sub-problems.The nonparametric Bayesian dictionary learning model is complex and difficult to directly solve the model parameters.Gibbs sampling is usedto iterate the model parameters in turn and we can obtain the optimal dictionary and sparse representation.Compared with GradDLRec algorithm,the whole algorithm has good denoising performance.Considering the structure information of the image,the image blocks are classified by clustering method,and the gradient domain image patch grouping Bayesian learning image denoising method is proposed.According to the nonlocal self-similarity of images,firstly,the method groups the image patches.Then we use the Gaussian mixture model to match the grouped patches of gradient images and get different patch components.Finally,the potential clean gradient image structure of each component is reconstructed by BPFA dictionary learning method.Structural clustering is applied to the BPFA dictionary learning model,and the whole image denoising model is greatly improved.