Deep Image Prior (DIP) offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In our work we boost DIP by adding an explicit prior based on Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. This software package reproduces the results we report in our DeepRED paper.