In this work, we are interested in gradient sparse + low patchrank signal recovery for image structure-texture decomposition. We locally model the structure as gradient-sparse and the texture as of low patch-rank. Moreover, we propose a rule based upon theoretical results of sparse + low-rank matrix recovery in order to automatically tune our model depending on the local content and we numerically validate this proposition.