Proximal Neural Network

Proximal neural networks (PNNs) combine deep learning with proximal optimization algorithms to solve inverse problems, particularly in image processing. Research focuses on improving training efficiency through methods like lifted Bregman strategies and developing novel architectures, such as rotation-equivariant proximal networks and learned proximal networks (LPNs), to enhance performance and interpretability in tasks like denoising and compressed sensing. This approach offers a powerful framework for tackling ill-posed problems by leveraging both the data-driven power of deep learning and the theoretical guarantees of optimization, leading to improved accuracy and robustness in various applications.