Comparison of Several Reconstruction Algorithms in Compressed Sensing

In this article, we will explore several reconstruction algorithms for compressed sensing, such as OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), SP (Subspace Pursuit), and others. These reconstruction algorithms enable the recovery of approximate original information from data under lossy compression conditions, playing a crucial role in data processing. The current algorithm collection includes extensive methods that can meet requirements across different scenarios. While the implementation approaches vary among these algorithms, they share a common objective: to accurately reconstruct original information while maintaining data compression. These algorithms typically involve iterative processes where key functions handle sparse signal recovery through techniques like greedy iterations, residual updates, and support set maintenance. The implementations often include parameter optimization for sparsity levels and error thresholds to balance reconstruction accuracy and computational efficiency. These algorithms find applications in multiple domains such as image processing, audio processing, and communications, making them highly promising for broad practical implementation.