基于自适应对偶字典的磁共振图像的超分辨率重建-CORE.PDFVIP

第28 卷第4 期 光电技术应用 Vol.28 ，No.4 2013 年8 月 ELECTRO-OPTIC TECHNOLOGY APPLICATION August ，2013 ·信号与信息处理· 基于自适应对偶字典的磁共振图像的超分辨率重建 刘振圻，包立君，陈 忠 （厦门大学电子科学系，福建 厦门 361005） 摘 要：为了提高磁共振成像的图像质量，提出了一种基于自适应对偶字典的超分辨率去噪重建方法，在超分辨率重建过程 中引入去噪功能，使得改善图像分辨率的同时能够有效地滤除图像中的噪声，实现了超分辨率重建和去噪技术的有机结合。该 方法利用聚类—PCA 算法提取图像的主要特征来构造主特征字典，采用训练方法设计出表达图像细节信息的自学习字典，两者 结合构成的自适应对偶字典具有良好的稀疏度和自适应性。实验表明，与其他超分辨率算法相比，该方法超分辨率重建效果显 著，峰值信噪比和平均结构相似度均有所提高。 关键字：稀疏表示；自适应对偶字典；超分辨率重建；去噪；磁共振成像 中图分类号：R445.2 文献标识码：A 文章编号：1673-1255（2013 ）-04-0055-06 Super-resolutionReconstructionforMagneticResonanceImagingBasedon AdaptiveDualDictionary LIU Zhen-qi, BAO Li-jun, CHEN Zhong (Department of Electronic Science, Xiamen University, Xiamen 361005, China) Abstract:In order to enhance images quality of magnetic resonance imaging (MRI), a super-resolution de⁃ noising reconstruction method is proposed based on adaptive dual dictionary. In the method, denoising function is used in super-resolution reconstruction process so that the noise in images is filtered effectively as the improve⁃ ment of image resolution. And the integration of super-resolution reconstruction and denoising technology is im⁃ plemented. Clustering-PCA algorithm is used in the method to extract main features of images to construct main-feature dictionary. Training method is used to design self-learning dictionary expressing detailed informa⁃ tion of images. Adaptive dual dictionary constructed by combination of the two dictionaries has good sparseness and adaptability. Experimental results show that super-resolution reconstruction effect is remarkable in the meth⁃ od comparing with other super-resolution algorithms. Peak signal to no