The inadequacies of the current generation of deconvolution algorithms are rapidly becoming apparent as new, more sensitive radio interferometers are constructed. In light of these inadequacies, there is renewed interest in the field of deconvolution. Many new algorithms are being developed using the mathematical framework of compressed sensing. One such technique, MORESANE, has recently been shown to be a powerful tool for the recovery of faint difuse emission from synthetic and simulated data. However, the original implementation is not well-suited to large problem sizes due to its computational complexity. Additionally, its use of proprietary software prevents it from being freely distributed and used. This has motivated the development of a freely available Python implementation, PyMORESANE. This thesis describes the implementation of PyMORESANE as well as its subsequent augmentation with MPU and GPGPU code. These additions accelerate the algorithm and thus make it competitive with its legacy counterparts. The acceleration of the algorithm is verified by means of benchmarking tests for varying image size and complexity. Additionally, PyMORESANE is shown to work not only on synthetic data, but on real observational data. This verification means that the MORESANE algorithm, and consequently the PyMORESANE implementation, can be added to the current arsenal of deconvolution tools.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:rhodes/vital:5563 |
| Date | January 2015 |
| Creators | Kenyon, Jonathan |
| Publisher | Rhodes University, Faculty of Science, Physics and Electronics |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Masters, MSc |
| Format | 95 leaves, pdf |
| Rights | Kenyon, Jonathan |
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