Plug-and-Play (PnP) is an image restoration framework which leverages on a set of powerful denoisers to induce a priori information on the solution of a general image restoration problem. By using splitting techniques, such as the Half Quadratic Splitting (HQS), the PnP framework solves the classical regularized optimization problem where the regularizer-related step is replaced by a denoiser. In this talk, I will introduce HQS - Deep PnP which is mainly focus on Convolutional Neural Network (CNN) denoisers. I will show how increase the performances of PnP by considering a general framework where the denoiser act on a transformation of the image (e.g. the discrete gradient) and a further handcrafted regularization term is added (e.g. Total Variation). Moreover, I will prove that for the HQS - Deep PnP algorithm a fixed point convergence is guaranteed under certain assumptions. The good performances of the proposed approach for the task of non-blind denoising and deblurring are addressed through several experiments both on synthetic and real data.