Because detail features may be coverd by the feature components with large ampli tude in process of choosing spatialneighborhood in image super-resolution (SR) reconstruction,a direction ditictionary and neighbor embedding based (DD-NE) SR reconsturction algorithm is proposed. image SR reconstruction using signal processing method The image resolution is improved in.The input image and the neighborhood are sparsely decomposed by using the clustering dictionary.The feature subgraphs with large and small amplitudes are reconstructed from the lar ge and small amplitude values,respectively,The low resolution (LR) image librar y and the input image are represented by a clustering dictionary.The detail signal is expressed in the fo rm of dictionary atoms,and the metric of spatial neighborhood is converted into the measure of dictionary atoms .Experimental results show that compared with NE algorithm,DD-NE reconstruction of algorithm can improve the av erage PSNR by 1.1dB,which greatly improves the reconstruction performance,but reconstruction time is only 30.9% of that of NE algorithm.