MATLAB Implementation of Compressed Sensing Algorithms
Comprehensive compressed sensing code featuring multiple selectable measurement matrices for signal reconstruction applications
Testing the Restricted Isometry Property (RIP) of Measurement Matrices in Compressed Sensing
This resource demonstrates how to construct and validate measurement matrices satisfying the Restricted Isometry Property (RIP) for compressed sensing applications, including implementation approaches and verification methods.
Calculating RIP Factors for Measurement Matrices and Sparsity Bases in Compressed Sensing
Computational methods for determining Restricted Isometry Property (RIP) factors of measurement matrices and sparsity bases in compressed sensing systems, with implementation considerations.
Compressed Sensing of Lena Image using Hadamard Measurement Matrix and OMP Reconstruction Algorithm
This program implements compressed sensing for the Lena image, employing a Hadamard measurement matrix for acquisition and Orthogonal Matching Pursuit (OMP) algorithm for reconstruction, demonstrating efficient signal recovery with reduced sampling requirements.