A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region accor...A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.展开更多
According to the Collins’ formula and its inverse diffraction computation,this paper presents an algorithm for reconstructing the object wavefront of a numerical holograph.To improve the quality of wavefront reconstr...According to the Collins’ formula and its inverse diffraction computation,this paper presents an algorithm for reconstructing the object wavefront of a numerical holograph.To improve the quality of wavefront reconstruction,a technique of eliminating the zero-order diffraction light is introduced,which is achieved by subtraction of two intensities of the interference patterns.The computer simulation and image processing show that the method of eliminating the zero-order diffraction beam can be applied to the wavefront reconstruction of inverse diffraction computation.展开更多
文摘A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.
基金Supported bythe Natural Science Foundation of Yunnan Province(2004F0025 M)
文摘According to the Collins’ formula and its inverse diffraction computation,this paper presents an algorithm for reconstructing the object wavefront of a numerical holograph.To improve the quality of wavefront reconstruction,a technique of eliminating the zero-order diffraction light is introduced,which is achieved by subtraction of two intensities of the interference patterns.The computer simulation and image processing show that the method of eliminating the zero-order diffraction beam can be applied to the wavefront reconstruction of inverse diffraction computation.
