Multi-scale　structural　self-similarity　refers　to　that　there　are　many　similar　structures　in　the　same　image,　which　are　either　in　the　same　scale　or　across　different　scales.　In　this　paper,　a　single-image　super-resolution　method　based　on　multi-scale　nonlocal　regularization　is　proposed.　In　this　method,　the　multi-scale　nonlocal　and　the　multi-scale　dictionary　learning　methods　are　combined　to　add　the　extra　information　exploited　from　multi-scale　similar　structures　into　the　reconstructed　image.　The　multi-scale　nonlocal　method　exploits　extra　information　from　multi-scale　similar　structures　by　searching　for　similar　patches　in　the　image　pyramid　and　constructing　the　multi-scale　nonlocal　regularization　according　to　the　correspondence　between　multi-scale　similar　patches.　The　multi-scale　dictionary　learning　method　exploits　extra　information　from　multi-scale　similar　structures　by　using　the　image　pyramid　as　training　samples　in　dictionary　learning,　so　that　the　patches　in　the　pyramid　have　sparse　representations　over　the　learned　dictionary.　Experimental　results　demonstrate　that　the　method　achieves　better　image　quality　compared　with　ScSR,　SISR,　NLIBP,　CSSS,　ASDSAR　and　mSSIM　methods.　Copyright　©　2014　Acta　Automatica　Sinica.　All　rights　reserved.