Sparse Signal

Sparse signal processing focuses on efficiently representing and recovering signals with only a few non-zero components, aiming to reduce computational complexity and data storage while preserving essential information. Current research emphasizes developing robust algorithms for sparse signal reconstruction in various noisy and complex environments, including the use of compressed sensing, deep learning models (like deep equilibrium networks and transformers), and iterative methods such as orthogonal matching pursuit and approximate message passing. These advancements have significant implications for diverse fields, such as medical imaging, signal processing, and machine learning, enabling improved efficiency and accuracy in data analysis and signal recovery.