Sketch-photo synthesis is one of the important research issues of heterogeneous image transformation. Some available popular synthesis methods, like locally linear embedding (LLE), usually generate sketches or photos with lower definition and blurred details, which reduces the visual quality and the recognition rate across the heterogeneous images. In order to improve the quality of the synthesized images, a multi-dictionary sparse representation based face sketch-photo synthesis model is constructed. In the proposed model, LLE is used to estimate an initial sketch or photo, while the multi-dictionary sparse representation model is applied to generate the high frequency and detail information. Finally, by linear superimposing, the enhanced face sketch or photo can be obtained. Experimental results show that sketches and photos synthesized by the proposed method have higher definition and much richer detail information resulting in a higher face recognition rate between sketches and photos.