deconvolve – Image based deconvolver – imaging task

Description

Several algorithms are available to deconvolve an image with a known psf (dirty beam), or a Gaussian beam. The algorithms available are clark and hogbom clean, a multiscale clean and a mem clean.

NOTE: Recommend using taskname=clean if psf is a dirty beam

Parameters

Title
Parameter	Default	Description
imagename	`''`
model	`''`
psf	`numpy.array( [ ] )`
alg	`'clark'`
niter	`int(10)`
gain	`float(0.1)`
threshold	`{'value': float(0.0), 'unit': 'mJy'}`
mask	`''`
scales	`numpy.array( [ int(0),int(3),int(10) ] )`
sigma	`{'value': float(0.0), 'unit': 'mJy'}`
targetflux	`{'value': float(1.0), 'unit': 'Jy'}`
prior	`''`

Parameter Explanations

imagename

''

Input image to deconvolve

model

''

Output image containing deconvolved point model

psf

numpy.array( [ ] )

Point spread function (dirty beam)

alg

'clark'

Algorithm to use (clark, hogbom, multiscale, mem)

niter

int(10)

number of iteration in deconvolution process

gain

float(0.1)

CLEAN gain parameter

threshold

{'value': float(0.0), 'unit': 'mJy'}

level below which sources will not be deconvolved

mask

''

image mask to limit region of deconvolution

scales

numpy.array( [ int(0),int(3),int(10) ] )

scale sizes (pixels) to deconvolve

sigma

{'value': float(0.0), 'unit': 'mJy'}

mem parameter: Expected noise in image

targetflux

{'value': float(1.0), 'unit': 'Jy'}

mem parameter: Estimated total flux in image

prior

''

mem parameter: prior image for mem search