Deep Unfolding

Deep unfolding leverages the strengths of both model-based and data-driven approaches by unfolding iterative optimization algorithms into deep neural networks. Current research focuses on applying this technique to diverse inverse problems, including image and video reconstruction, hyperspectral imaging, and robotic manipulation, often employing architectures based on Alternating Direction Method of Multipliers (ADMM) or proximal gradient descent. This approach offers improved interpretability and efficiency compared to purely data-driven methods, leading to advancements in various fields ranging from medical imaging to computer vision and beyond.