Compressed Sensing

Compressed sensing (CS) aims to reconstruct high-dimensional signals from a significantly smaller number of measurements than traditionally required, leveraging the inherent sparsity or low-dimensional structure of many real-world signals. Current research heavily emphasizes deep learning approaches, particularly unrolled optimization algorithms and generative models like diffusion networks, to improve reconstruction accuracy and speed, often outperforming traditional optimization methods. CS finds applications across diverse fields, including medical imaging (MRI, CT), signal processing, and remote sensing, offering significant potential for reducing data acquisition time, storage needs, and computational costs. The development of robust and efficient algorithms, especially those adaptable to various signal types and measurement conditions, remains a key focus.