Name
Abstract | ||
|---|---|---|
Current and future radio telescopes deal with large volumes of data and are expected to generate high resolution gigapixel-size images. The imaging problem in radio interferometry is highly ill-posed and the choice of prior model of the sky is of utmost importance to guarantee a reliable reconstruction. Traditionally, one or more regularization terms (e.g. sparsity and positivity) are applied for the complete image. However, radio sky images can often contain individual source facets in a large empty background. More precisely, we propose to divide radio images into source occupancy regions (facets) and apply relevant regularizing assumptions for each facet. Leveraging a stochastic primal dual algorithm, we show the potential merits of applying facet-based regularization on the radio-interferometric images which results in both computation time and memory requirement savings. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553515 | European Signal Processing Conference |
Field | DocType | ISSN |
Radio astronomy,Iterative reconstruction,Diffusing update algorithm,Computer science,Algorithm,Radio telescope,Regularization (mathematics),Facet (geometry),Computation,Scalability | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shahrzad Naghibzadeh | 1 | 0 | 1.01 |
| Audrey Repetti | 2 | 76 | 6.84 |
| Veen, Alle-Jan van der | 3 | 4 | 3.64 |
| Yves Wiaux | 4 | 194 | 25.03 |