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Compressed sensing is an approach to signal processing that allows for signals and images to be reconstructed with lower sampling rates than with Nyquist’s Law. This makes signal processing and reconstruction much simpler and has a wide variety of applications in the real world, including photography, holography and facial recognition.
Compressed sensing is also known as compressive sensing, compressive sampling and sparse sampling.
The Nyquist-Shannon sampling theorem states that a signal can be reconstructed perfectly if the highest frequency is less than half the sampling rate. In 2004, researchers found that with knowledge about a signal’s sparsity, a signal can be reconstructed with even fewer samples, a process called compressed sensing. The lower sampling rate makes storing and processing this data much more efficient.
Some of the applications of this insight include mobile phone cameras, holography, facial recognition, medical imaging, network tomography and radio astronomy.