Spatial　resolution　enhancement　of　hyperspectral　images　is　one　of　the　key　and　difficult　topics　in　the　field　of　imaging　spectrometry.　The　redundant　dictionary　based　sparse　representation　theory　is　introduced,　and　a　spatial　resolution　enhancement　algorithm　is　proposed.　In　this　algorithm,　a　pixel　curve　instead　of　a　pixel　patch　is　taken　as　the　unit　of　processing.　A　pair　of　low-　and　high-resolution　respective　redundant　dictionaries　are　joint　trained,　with　the　constraint　that　a　pair　of　high-　and　low-resolution　corresponded　pixel　curves　can　be　sparse　represented　by　same　coefficients　according　to　the　respected　dictionaries.　In　the　process　of　super-resolution　restoration,　the　low-resolution　hyperspectral　image　is　first　sparse　decomposed　based　on　the　low-resolution　redundant　dictionary　and　then　the　obtained　coefficients　are　used　to　reconstruct　the　corresponding　high-resolution　image　with　respect　to　the　high-resolution　dictionary.　The　maximum　a　posteriori　based　constrained　optimization　is　performed　to　further　improve　the　quality　of　the　reconstructed　high-frequency　information.　Experimental　results　show　that　the　pixel　curve　based　sparse　representation　is　more　suitable　for　a　hyperspectral　image;　the　highly　spectral　correlations　are　better　used　for　resolution　enhancement.　In　comparison　with　the　traditional　bilinear　interpolation　method　and　other　referenced　super-resolution　algorithms,　the　proposed　algorithm　is　superior　in　both　objective　and　subjective　results.　(C)　The　Authors.　Published　by　SPIE　under　a　Creative　Commons　Attribution　3.0　Unported　License.