Compressed sensing (CS) is a technique in signal processing that enables the reconstruction of a signal from a small number of samples. It leverages the idea that many signals are sparse or can be sparsely represented in some basis, meaning that they contain significant information in far fewer dimensions than they are originally represented in. ### Key Concepts of Compressed Sensing: 1. **Sparsity**: A signal is considered sparse if it has a representation in a transformed domain (e.g.
Articles by others on the same topic
Compressed sensing reconstructs a sparse vector, or an approximately sparse one, from fewer linear measurements than its ambient dimension. Basis pursuit replaces counting nonzero coordinates by convex minimization. A null space property characterizes exact uniform recovery, while a robust null space property controls errors from noise and nonsparse tails.