Monotone Operator Learning

Monotone Operator Learning (MOL) is a deep learning framework designed to improve the efficiency and robustness of model-based image reconstruction methods, particularly in high-dimensional applications like magnetic resonance imaging (MRI). Current research focuses on developing MOL algorithms using convolutional neural networks (CNNs), often within a deep equilibrium (DEQ) model architecture, to achieve convergence guarantees and memory efficiency compared to traditional unrolled methods. This approach offers advantages in terms of computational cost and robustness to noise, leading to improved image quality and potentially faster clinical workflows. The resulting algorithms are showing promise for accelerating MRI reconstruction and other image recovery tasks.