In this paper, we propose a novel neighbor embedding method based on joint sub-bands for image super-resolution. Rather than directly reconstructing the total spatial variations of the input image, we restore each frequency component separately. The input LR image is decomposed into sub-bands defined by steerable filters to capture structural details on different directional frequency components. Then the neighbor embedding principle is employed to reconstruct each band, respectively. Moreover, taken the diverse characteristics of each band into account, we adopt adaptive similarity criteri-ons for searching nearest neighbors. Finally, we recombine the generated HR sub-bands by applying the inverting subband decomposition to get the final super-resolved result. Experimental results demonstrate the effectiveness of our method both in objective and subjective qualities comparing with other state-of-the-art methods.