Editing Super-resolution imaging (section)

{{short description|Any technique to improve resolution of an imaging system beyond conventional limits}}
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'''Super-resolution imaging''' ('''SR''') is a class of techniques that improve the [[image resolution|resolution]] of an [[digital imaging|imaging]] system. In '''optical SR''' the [[diffraction-limited|diffraction limit]] of systems is transcended, while in '''geometrical SR''' the resolution of digital [[image sensor|imaging sensors]] is enhanced.

In some [[radar]] and [[sonar]] imaging applications (e.g. [[magnetic resonance imaging]] (MRI), [[high-resolution computed tomography]]), [[space (mathematics)|subspace]] decomposition-based methods (e.g. [[MUSIC (algorithm)|MUSIC]]<ref>Schmidt, R.O, "Multiple Emitter Location and Signal Parameter Estimation," IEEE Trans. Antennas Propagation, Vol. AP-34 (March 1986), pp.276-280.</ref>) and [[compressed sensing]]-based algorithms (e.g., [[SAMV (algorithm)|SAMV]]<ref name=AbeidaZhang>{{cite journal | last1=Abeida | first1=Habti | last2=Zhang | first2=Qilin | last3=Li | first3=Jian|author3-link=Jian Li (engineer) | last4=Merabtine | first4=Nadjim | title=Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing | journal=IEEE Transactions on Signal Processing | volume=61 | issue=4 | year=2013 | issn=1053-587X | doi=10.1109/tsp.2012.2231676 | pages=933–944 | url=https://qilin-zhang.github.io/_pages/pdfs/SAMVpaper.pdf | bibcode=2013ITSP...61..933A | arxiv=1802.03070 | s2cid=16276001 }}</ref>) are employed to achieve SR over standard [[periodogram]] algorithm.

Super-resolution imaging techniques are used in general [[image processing]] and in [[super-resolution microscopy]].