Compressive Sensing Algorithms: Comparative Implementation and Analysis
Application Context Compressive sensing represents a highly valuable source code implementation with significant practical applications in signal processing, image reconstruction, and communication systems. This program provides comparative analysis of multiple algorithms, making it particularly valuable for researchers beginning their exploration of compressive sensing. The implementation demonstrates practical utility while maintaining research-oriented flexibility for algorithm modification and performance evaluation. Key Technologies The codebase implements and compares various compressive sensing algorithms including greedy approaches (OMP, CoSaMP), convex optimization methods (l1-minimization), and iterative thresholding techniques. Each algorithm is implemented with clear parameter configurations and performance metrics to facilitate understanding of trade-offs between reconstruction accuracy and computational complexity.