The GitHub repository contains MATLAB source code for Compressive Sensing via Belief Propagation developed by Dr. Dror Baron, which can be found at https://people.engr.ncsu.edu/dzbaron/software/CSBP/. This tool uses Low-Density Parity-Check (LDPC) codes as sparse measurement matrices to minimize complexity and sufficient statistics.
Compressive Sensing via Belief Propagation is a powerful technique that allows the recovery of sparse signals from a small number of linear measurements. The technique uses LDPC codes as measurement matrices, which are known for their sparsity and ability to achieve near-optimal performance in compressive sensing applications.
With this MATLAB source code, you can implement Compressive Sensing via Belief Propagation using LDPC codes and recover sparse signals from noisy data. The tool provides an efficient and effective way to perform compressive sensing with high accuracy and low complexity.
Overall, this repository provides a valuable resource for researchers and practitioners working on compressive sensing applications, particularly those using LDPC codes as measurement matrices.