计算机工程2013，Vol.39Issue(7)：270-273,4.DOI:10.3969/j.issn.1000-3428.2013.07.060
基于字典学习正则化的图像去噪
Image Denoising Based on Dictionary Learning Regularization
摘要
Abstract
For the sparse characteristic and maintaining features characteristic,the sparse representation is widely used in image processing.To solve the problem of image denoising in the area of image processing,this paper proposes a new Bayesian denoising model based on image feature sparse representation.The model uses the K-means and Principal Component Analysis(PCA) method to obtain the coefficients of dictionary for sparse representation solutions of image patches.The coefficients solutions are used to train the dictionary with regularized optimization.The alternating minimizations are kept between above two steps until the difference between the image dictionary and the source image dictionary satisfied a convergence criterion.It restores the denoising image under the MAP model with that dictionary.Experimental results show that the higher Peak Signal to Noise Ratio(PSNR) value than the source noised images with the increase of imposed noise into clean images,comparing to the initialization with Discrete Cosine Transform(DCT).关键词
图像去噪/字典学习/贝叶斯模型/稀疏表示/正则化/高斯噪声Key words
image denoising/ dictionary learning/ Bayesian model/ sparse representation/ regularization/ Gauss noise分类
信息技术与安全科学引用本文复制引用
郑兴明,刘宁钟..基于字典学习正则化的图像去噪[J].计算机工程,2013,39(7):270-273,4.基金项目
国家自然科学基金资助项目(60903104) （60903104）
中央高校基本科研业务费专项基金资助项目(kfjj20110241) （kfjj20110241）
