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 algorithm based on image analogies in NSCT domain is proposed. Firstly, NSCT is performed on the original image and the degraded image at different scales and directions, thus low-pass subband pair and varieties of directional bandpass subband pairs are obtained. Then the high resolution low-pass subband and varieties of directional bandpass subband are generated by using image self-analogies. Finally, the super-resolution reconstructed image is obtained by transforming these subband coefficients back to the spatial domain. The experimental results show that the algorithm can be executed independently without any supposed outliers and it can compute much more sharply than general image analogies methods. It also can generate more reasonable details than general image analogies methods, thus the edges are much clearer and the image is more natural-looking.