The `CLEAN' algorithm

 

The `CLEAN' algorithm, introduced by Högbom (1974), assumes that the radio sky can be represented by a small number of point sources in an otherwise empty field of view. It uses a simple iterative procedure to find the positions and strengths of these sources. The final deconvolved `CLEAN' image is the sum of:

1. these point-source `CLEAN components' reconvolved (``restored'') with a `CLEAN beam' (usually Gaussian) to de-emphasize the higher spatial frequencies which are often spuriously extrapolated and,
2. (optionally, but strongly recommended) an image representing residual differences between the point-source model and the data.

We now describe some variants of the `CLEAN' algorithm, including two that can be used on large images.

• The Högbom algorithm
• The Clark algorithm
• The Cotton-Schwab algorithm
• Other algorithms related to `CLEAN'
• Practical details and problems of `CLEAN'
• The use of boxes: finite support
• Number of iterations and the `CLEAN' loop gain
• The problem of short spacings
• The `CLEAN' beam
• Use of a priori models in `CLEAN'
• Non-uniqueness of `CLEAN' images
• Instabilities

1996 November 4
10:52:31 EST