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.