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.