We show in this talk how proximal algorithms, which constitute a powerful class of optimization methods, can be unfolded under the form of deep neural networks. This yields to improved performance and faster implementations while allowing to build more explainable, more robust, and more insightful neural network architectures. Application examples in the domain of image restoration will be provided.