13 Facts About Compressed sensing

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems.

Compressed sensing relies on techniques, which several other scientific fields have used historically.

At first glance, compressed sensing might seem to violate the sampling theorem, because compressed sensing depends on the sparsity of the signal in question and not its highest frequency.

Compressed sensing takes advantage of the redundancy in many interesting signals—they are not pure noise.

Compressed sensing typically starts with taking a weighted linear combination of samples called compressive measurements in a basis different from the basis in which the signal is known to be sparse.

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Field of compressive sensing is related to several topics in signal processing and computational mathematics, such as underdetermined linear-systems, group testing, heavy hitters, sparse coding, multiplexing, sparse sampling, and finite rate of innovation.

Imaging techniques having a strong affinity with compressive Compressed sensing include coded aperture and computational photography.

Compressed sensing is used in single-pixel cameras from Rice University.

Compressed sensing can be used to improve image reconstruction in holography by increasing the number of voxels one can infer from a single hologram.

Compressed sensing has been used to shorten magnetic resonance imaging scanning sessions on conventional hardware.

Compressed sensing has showed outstanding results in the application of network tomography to network management.

Compressed sensing combined with a moving aperture has been used to increase the acquisition rate of images in a transmission electron microscope.

In scanning mode, compressive Compressed sensing combined with random scanning of the electron beam has enabled both faster acquisition and less electron dose, which allows for imaging of electron beam sensitive materials.