Everything about Compressed sensing
Signal processing technique
Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity, which requires the signal to be sparse in some domain. The second one is incoherence, which is applied through the isometric property, which is sufficient for sparse signals. Compressed sensing has applications in, for example, magnetic resonance imaging (MRI) where the incoherence condition is typically satisfied. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Compressed sensingA 5-minute orientation
- Skim a recent paper: Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive ReviewInfluential work since 2022
- Pick up a classic book: Sparse image and signal processingThe most-reprinted book on the subject
- Read the landmark paper: Compressed sensingCited 23,581 times
- Go sideways: Sparse dictionary learningA closely related topic
- Go sideways: Sparse approximationA closely related topic
📖 Definitions
Noun A signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Compressed sensing
Sparse MRI: The application of compressed sensing for rapid MR imaging
Compressive Sensing [Lecture Notes]
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
The restricted isometry property and its implications for compressed sensing
Bayesian Compressive Sensing
🆕 Influential since 2022
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive Review
A Comprehensive Review of Graphitic Carbon Nitride (g-C3N4)–Metal Oxide-Based Nanocomposites: Potential for Photocatalysis and Sensing
Deep learning in multimodal remote sensing data fusion: A comprehensive review
Content-Aware Scalable Deep Compressed Sensing
Compressed sensing in fluorescence microscopy
📚 Books worth reading
Sparse image and signal processingJean-Luc Starck, Fionn Murtagh · 2010- 📕Compressed sensingYonina C. Eldar · 2012
Compressive Sensing for Urban RadarMoeness Amin · 2014
Optical and digital image processingGabriel Cristóbal, Peter Schelkens · 2008- 📕Compressed Sensing for EngineersAngshul Majumdar · 2018
- 📕Compressive Sensing of Earth ObservationsC. H. Chen · 2017
- 📕Optical Compressive ImagingAdrian Stern · 2016
Compressive Sensing for Wireless NetworksHusheng Li · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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