Name
Title | ||
|---|---|---|
Colour compressed sensing imaging via sparse difference and fractal minimisation recovery |
Abstract | ||
|---|---|---|
In colour compressed sensing (CS) imaging, the current two bottlenecks for application are (1) high computation cost of sparse representation (SR) with over-complete dictionary and (2) unsatisfactory imaging quality of CS recovery with l1-norm minimisation. Thus, this study proposes a novel colour CS imaging framework. In the framework, two improvements are achieved: (1) the authors present the sparse difference to reduce the computation cost of SR in RGB colour imaging; (2) the authors use fractal dimension instead of l1-norm as the object function to actualise high quality CS recovery. The feasibility of our colour CS imaging framework is proved by sseveral experiments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-ipr.2014.0346 | IET Image Processing |
Field | DocType | Volume |
Computer vision,Pattern recognition,Fractal dimension,Sparse approximation,Fractal,Object function,Minimisation (psychology),Artificial intelligence,RGB color model,Mathematics,Compressed sensing,Computation | Journal | 9 |
Issue | ISSN | Citations |
5 | 1751-9659 | 2 |
PageRank | References | Authors |
0.39 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jixin Liu | 1 | 32 | 9.23 |
| Xiaofei Li | 2 | 3 | 3.40 |
| Guang Han | 3 | 16 | 4.94 |
| Ning Sun | 4 | 118 | 13.20 |
| Kun Du | 5 | 33 | 7.22 |
| Quansen Sun | 6 | 1222 | 83.09 |