In lots of cases, it′s difficult to obtain appropriate image training set, but single-image zooming is an ill-posed problem. Using the self-similarity feature among local structure in an image which can be maintained in the scale space and the advantage of nonsubsampled contourlet transform (NSCT), a single image super-resolution reconstruction (SRR) algorithm based on image analogies and NSCT is proposed. NSCT is performed on the original image and the degraded image at different scales and directions, thus varieties of directional bandpass subband pairs are obtained. The relationships between the subband pairs by image self-analogies are learned to generate high resolution varieties of directional bandpass subband. The super-resolution reconstructed image is obtained by transforming these changed subband coefficients and the zoomed-original image by bicubic interpolation back to the spatial domain. The experimental results show that the algorithm can be executed independently without any supposed outliers. It can generate more reasonable details than general methods, thus the edges are much clearer and the image is more natural-looking.