| Title | A Review of Reconstruction Algorithms in Compressive Sensing |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Manchanda, R., Sharma, K. |
| Conference Name | 2020 International Conference on Advances in Computing, Communication Materials (ICACCM) |
| Keywords | composability, compressed sensing, compressive sampling, compressive sensing, Cyber-physical systems, Image reconstruction, Iterative algorithms, Matching pursuit algorithms, Measurements, Optimization, privacy, pubcrawl, Reconstruction algorithms, Resiliency, sparse signal |
| Abstract | Compressive Sensing (CS) is a promising technology for the acquisition of signals. The number of measurements is reduced by using CS which is needed to obtain the signals in some basis that are compressible or sparse. The compressible or sparse nature of the signals can be obtained by transforming the signals in some domain. Depending on the signals sparsity signals are sampled below the Nyquist sampling criteria by using CS. An optimization problem needs to be solved for the recovery of the original signal. Very few studies have been reported about the reconstruction of the signals. Therefore, in this paper, the reconstruction algorithms are elaborated systematically for sparse signal recovery in CS. The discussion of various reconstruction algorithms in made in this paper will help the readers in order to understand these algorithms efficiently. |
| DOI | 10.1109/ICACCM50413.2020.9212838 |
| Citation Key | manchanda_review_2020 |
A Review of Reconstruction Algorithms in Compressive Sensing