基于自适应对偶字典的磁共振图像的超分辨率重建

Super-resolution Reconstruction for Magnetic Resonance Imaging Based on Adaptive Dual Dictionary

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。

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 noise ratio（PSNR） and mean structure simi larity（MSSIM） are all improved.