Review of compressed sensing in imaging : algorithms and applications
Compressed sensing is a fast growing field in signal and image processing. If x is a given vector which can either be an image or a signal about which we have a prior knowledge that it is sparse in either of the basis, then this signal x can be reconstructed from much lesser measurements than the nu...
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2013
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| Online Access: | http://hdl.handle.net/10356/54336 |
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sg-ntu-dr.10356-543362023-03-03T16:07:07Z Review of compressed sensing in imaging : algorithms and applications Raviselvam Sujithra Liu Quan School of Chemical and Biomedical Engineering DRNTU::Engineering::Bioengineering Compressed sensing is a fast growing field in signal and image processing. If x is a given vector which can either be an image or a signal about which we have a prior knowledge that it is sparse in either of the basis, then this signal x can be reconstructed from much lesser measurements than the number of measurements which usually is considered to be necessary to give proper reconstruction. This can be done by using a measurements or sensing matrix of order m x n which is independently and identically distributed (IID) for which m<<n. This paper will review compressed sensing technique, steps involved in it and multiple algorithms that can be used to implement those steps and also representative applications of compressed sensing. Master of Science 2013-06-19T04:04:16Z 2013-06-19T04:04:16Z 2013 2013 Thesis http://hdl.handle.net/10356/54336 en 57 p. application/pdf |
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Singapore Singapore |
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English |
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DRNTU::Engineering::Bioengineering |
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DRNTU::Engineering::Bioengineering Raviselvam Sujithra Review of compressed sensing in imaging : algorithms and applications |
| description |
Compressed sensing is a fast growing field in signal and image processing. If x is a given vector which can either be an image or a signal about which we have a prior knowledge that it is sparse in either of the basis, then this signal x can be reconstructed from much lesser measurements than the number of measurements which usually is considered to be necessary to give proper reconstruction. This can be done by using a measurements or sensing matrix of order m x n which is independently and identically distributed (IID) for which m<<n. This paper will review compressed sensing technique, steps involved in it and multiple algorithms that can be used to implement those steps and also representative applications of compressed sensing. |
| author2 |
Liu Quan |
| author_facet |
Liu Quan Raviselvam Sujithra |
| format |
Theses and Dissertations |
| author |
Raviselvam Sujithra |
| author_sort |
Raviselvam Sujithra |
| title |
Review of compressed sensing in imaging : algorithms and applications |
| title_short |
Review of compressed sensing in imaging : algorithms and applications |
| title_full |
Review of compressed sensing in imaging : algorithms and applications |
| title_fullStr |
Review of compressed sensing in imaging : algorithms and applications |
| title_full_unstemmed |
Review of compressed sensing in imaging : algorithms and applications |
| title_sort |
review of compressed sensing in imaging : algorithms and applications |
| publishDate |
2013 |
| url |
http://hdl.handle.net/10356/54336 |
| _version_ |
1759857650312413184 |