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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| 格式: | Theses and Dissertations |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/54336 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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