We use sparse dictionary learning to develop transformations between seismic velocity models of different resolution and spatial extent. Starting with results in the common region of both models, the method can be used to enhance a regional lower-resolution model to match the style and resolution of local higher-resolution results while preserving its regional coverage. The method is demonstrated by applying it to two-dimensional Vs and three-dimensional VP and VS local and regional velocity models in southern California. The enhanced reconstructed models exhibit clear visual improvements, especially in the reconstructed VP/VS ratios, and better correlations with geological features. We demonstrate the improvements of the reconstructed model relative to the original velocity model by comparing waveform simulation results to observations. The improved fitting to observed waveforms extends beyond the domain of the overlapping region. The developed dictionary learning approach provides physically interpretable results and offers a powerful tool for additional applications for data enhancement in earth sciences.