Deep Unrolled

Deep unrolling is a machine learning technique that transforms iterative optimization algorithms into deep neural networks, aiming to leverage the strengths of both model-based and data-driven approaches for solving inverse problems. Current research focuses on improving the efficiency and robustness of these networks, exploring architectures like learned primal-dual networks and incorporating techniques such as wavelet transforms, momentum acceleration, and dimensionality reduction to enhance performance in applications like image reconstruction and graph learning. This approach offers the potential for improved accuracy and computational efficiency in various fields, particularly medical imaging and signal processing, by combining the interpretability of iterative methods with the power of deep learning.