MATLAB Implementation of Compressed Sensing Algorithms
Comprehensive compressed sensing code featuring multiple selectable measurement matrices for signal reconstruction applications
Explore MATLAB source code curated for "测量矩阵" with clean implementations, documentation, and examples.
Comprehensive compressed sensing code featuring multiple selectable measurement matrices for signal reconstruction applications
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.
Computational methods for determining Restricted Isometry Property (RIP) factors of measurement matrices and sparsity bases in compressed sensing systems, with implementation considerations.
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.