Contourlet transform in image restoration is apt to bring pseudo-Gibbs phenomenon. Nonsubsampled Contourlet (NSCT) has better frequency selectivity and regularity compared with the contourlet transform, and it can overcome pseudo-Gibbs phenomenon. However, learning-based super-resolution need establish the relation of different resolutions. Different from Laplacian pyramid, result of NSCT at each layer is with the same size, but cannot establish multi-resolution pyramid, and it needs large amount of computation. According to the problem of NSCT in learning-based super-resolution, a face image super-resolution restoration algorithm based on INSCT is proposed. In order to represent face images features, the INSCT pyramid is established. For particularity of the face images, corresponding points in matching process are used. Experimental results show that this method has good performance, and achieves better results on subjective visual effects and on objective peak signal-to-noise ratio. And the results are closer to the real images.