A GAUSSIAN MIXTURE MODEL-BASED REGULARIZATION METHOD IN ADAPTIVE IMAGE RESTORATION

• 来源期刊: 电子科学学刊（英文版）
• 学科: 工学
• 年，卷（期）: 2007,（1）
• 页码范围: 83-89
• 页数: 7页
• 分类号: TN91
• 语种: 英文

摘要

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 parameters 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 generalization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.