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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Compressed sensing | 6/6 | https://en.wikipedia.org/wiki/Compressed_sensing | reference | science, encyclopedia | 2026-05-05T14:40:18.609631+00:00 | kb-cron |
ISTA FISTA SISTA ePRESS EWISTA EWISTARS etc. Compressed sensing addresses the issue of high scan time by enabling faster acquisition by measuring fewer Fourier coefficients. This produces a high-quality image with relatively lower scan time. Another application (also discussed ahead) is for CT reconstruction with fewer X-ray projections. Compressed sensing, in this case, removes the high spatial gradient parts – mainly, image noise and artifacts. This holds potential as one can obtain high-resolution CT images at low radiation doses (through lower current-mA settings).
=== Network tomography === Compressed sensing has shown outstanding results in the application of network tomography to network management. Network delay estimation and network congestion detection can both be modeled as underdetermined systems of linear equations where the coefficient matrix is the network routing matrix. Moreover, in the Internet, network routing matrices usually satisfy the criterion for using compressed sensing.
=== Shortwave-infrared cameras === In 2013 one company announced shortwave-infrared cameras which utilize compressed sensing. These cameras have light sensitivity from 0.9 μm to 1.7 μm, wavelengths invisible to the human eye.
=== Aperture synthesis astronomy === In radio astronomy and optical astronomical interferometry, full coverage of the Fourier plane is usually absent and phase information is not obtained in most hardware configurations. In order to obtain aperture synthesis images, various compressed sensing algorithms are employed. The Högbom CLEAN algorithm has been in use since 1974 for the reconstruction of images obtained from radio interferometers, which is similar to the matching pursuit algorithm mentioned above.
=== Transmission electron microscopy === 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 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.
== See also == Compressed sensing in speech signals Low-density parity-check code Noiselet Sparse approximation Sparse coding Verification-based message-passing algorithms in compressed sensing
== Notes ==
== References ==
== Further reading == "The Fundamentals of Compressive Sensing" Part 1, Part 2 and Part 3: video tutorial by Mark Davenport, Georgia Tech. at SigView, the IEEE Signal Processing Society Tutorial Library. Using Math to Turn Lo-Res Datasets Into Hi-Res Samples Wired Magazine article Compressive Sensing Resources at Rice University. Compressed Sensing Makes Every Pixel Count – article in the AMS What's Happening in the Mathematical Sciences series Wiki on sparse reconstruction