Name
Abstract | ||
|---|---|---|
Compressed Sensing (CS), as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension. In this paper, compressed sensing method is proposed to reduce the noise and reconstruct the image signal. Noise reduction and image reconstruction are formulated in the theoretical framework of compressed sensing using Basis Pursuit (BP) and Compressive Sampling Matching Pursuit (CoSaMP) algorithm when random measurement matrix is utilized to acquire the data. In this research we have evaluated the performance of our proposed image enhancement methods using the quality measure peak signal-to-noise ratio (PSNR). |
Year | Venue | Field |
|---|---|---|
2016 | UKSim | Matching pursuit,Iterative reconstruction,Noise reduction,Computer vision,Noise measurement,Computer science,Basis pursuit,Artificial intelligence,Nyquist rate,Compressed sensing,Sparse matrix |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sahar Ujan | 1 | 0 | 0.34 |
| Seyed Ghorshi | 2 | 17 | 5.17 |
| Majid Pourebrahim | 3 | 0 | 0.34 |
| Seyed Alireza Khoshnevis | 4 | 0 | 0.68 |