Compressed Sensing

What Does Compressed Sensing Mean?

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.

Techopedia Explains Compressed Sensing

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.


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Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…