MATLAB Implementation of Compressed Sensing Algorithms

This text discusses MATLAB implementation of compressed sensing techniques. The codebase includes various measurement matrices that can be selected for different compression scenarios. These measurement matrices play a critical role in enabling computers to accurately reconstruct original information from compressed images or videos. The implementation typically involves key functions for matrix generation such as Gaussian random matrices, Bernoulli matrices, and partial Fourier matrices, each with distinct properties affecting reconstruction performance. Although the code appears concise, it incorporates sophisticated algorithms including L1-minimization techniques, orthogonal matching pursuit (OMP), and basis pursuit denoising. The mathematical foundation involves sparse signal representation and restricted isometry property (RIP) validation. For mathematics or computer science professionals, the code structure with its matrix initialization, measurement processes, and reconstruction algorithms will be relatively straightforward to comprehend. However, users without background in optimization theory or sparse signal processing may require additional study to fully understand the implementation and effectively apply it to their projects.