In order to improve resolution of single frame image with severe degradation, we propose a novel super-resolution reconstruction framework via regularization restoration combined with learning-based sparse representation enhancement. To achieve enlargement and suppression of blurring and noise simultaneously, we carefully balance the data fidelity and the prior item using regularization parameter on the basis of verisimilar estimation of degradation. Based on the acquired relatively clean image and pre-constructed over-complete sparse representation dictionary, image resolution zooming with characteristic of edge-preserving can then be realized. Fundamentally, the output of preceding regularization reversion remarkably betters low-frequency quality of bicubic interpolation version in conventional learning-based super-resolution. Furthermore, the ridding of blur and noise can favorably weaken dependency of atoms to degraded information. Consequently, their combination of two techniques can remarkably eliminate blur and noise, and meanwhile, remove annoying edge artifacts of enlarged image. Experiment results demonstrate that the addressed approach produces visually pleasing magnification for blurry and noisy low-resolution image.