L1 Norm Least Squares Algorithm

This text presents a MATLAB implementation of the L1 Norm Least Squares algorithm, commonly employed in image reconstruction research. The algorithm demonstrates significant flexibility, allowing for customization according to various application requirements. The implementation typically involves solving optimization problems using techniques like linear programming or iterative shrinkage-thresholding algorithms (ISTA) to handle the non-differentiable L1 norm. Key functions may include sparse matrix operations, regularization parameter tuning, and convergence criteria implementation. Successful application of this algorithm requires deep understanding of its mathematical principles, including sparse signal recovery theories and convex optimization frameworks, coupled with proficient MATLAB programming skills. Users should thoroughly study the underlying algorithm mechanics and engage in practical experimentation to achieve optimal reconstruction results.