Compressed Sensing Reconstruction Algorithms

About Compressed Sensing Reconstruction Algorithms - Compressed Sensing (CS), also known as Compressive Sampling, is an emerging interdisciplinary field between mathematics and information science that has gained popularity in recent years. Proposed by researchers including Candès and Terence Tao, CS challenges conventional sampling and encoding techniques based on the Nyquist-Shannon sampling theorem. The core implementation involves sparse signal reconstruction through optimization algorithms like L1-minimization, with key functions including measurement matrix design and reconstruction solvers.