We present an angular superresolution method for light fields captured with a sparse camera array. Our method uses local dictionaries extracted from a sampling mask for upsampling a sparse light field to a dense light field by applying compressed sensing reconstruction. We derive optimal sampling masks by minimizing the coherence for representative global dictionaries. The desired output perspectives and the number of available cameras can be arbitrarily specified. We show that our method yields qualitative improvements compared to previous techniques.