Compressed sensing is a signal processing technique that aims to efficiently acquire and reconstruct signals using fewer samples than traditionally required. It relies on the assumption that the signal of interest is sparse or compressible, meaning that it can be represented using fewer measurements than its full length. By exploiting this sparsity, compressed sensing allows for high-quality signal reconstruction with a reduced number of measurements, making it applicable in various fields such as imaging, communications, and machine learning.