• Outline
• Glossary
• 6. Deconvolution in Imaging
  • Previous: 6.5 Source Finding and Detection
  • Next: Chapter 7: Observing Systems

6.x Further Reading and References

CLEAN Implementations

1. Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines ⤴: Original CLEAN paper, image-domain implementation (Högbom).
2. An efficient implementation of the algorithm 'CLEAN' ⤴: Gridded visibility-domain CLEAN (Clark).
3. Relaxing the isoplanatism assumption in self-calibration; applications to low-frequency radio interferometry ⤴: Visibility-domain CLEAN (Cotton-Schwab).
4. Multiscale CLEAN Deconvolution of Radio Synthesis Images ⤴: Multi-scale CLEAN using Gaussian functions for deconvolution.
5. WSCLEAN: an implementation of a fast, generic wide-field imager for radio astronomy ⤴: WSCLEAN deconvolver
6. A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry ⤴: multi-frequency synthesis
7. MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators. A sparse deconvolution algorithm for radio interferometric imaging ⤴: MORESANE, a wavelet-based deconvolution method for extended sources.

Widefield Methods

1. Radio-interferometric imaging of very large fields - The problem of non-coplanar arrays ⤴: facet imaging and 3-D Fourier transform imaging.

Image Quality Assessment

1. The NRAO VLA Sky Surveys ⤴: CLEAN bias.
2. Synthesis Imaging in Radio Astronomy II (Lecture 13) ⤴: dynamic range as a proxy for image quality.

Source Modelling

1. Shape Reconstruction and Weak Lensing Measurement with Interferometers: A Shapelet Approach ⤴: Shapelet basis set-based sky modeling.
2. Python-based Shapelets ⤴: Python module for using Shapelets in radio interferometry.
3. BIRO: Bayesian inference for radio observations ⤴: Model selection using Bayesian inference.

Next: Chapter 7: Observing Systems