Compressed sensing is a signal processing technique that reconstructs a signal from far fewer samples than classical sampling theory requires, exploiting the fact that many real-world signals are sparse in some basis. Reconstruction is typically posed as a convex optimisation problem that recovers the sparsest signal consistent with the observed measurements. It has applications in medical imaging, wireless communications and any domain where sampling is costly or constrained.