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Subsections

Image and Spectral Image Formation

Imaging and Image Processing

In this section we consider the formation of images from edited, calibrated data. While this is mainly image computation and deconvolution, it must be remembered, that for the user, imaging and image deconvolution is an integral part of the process of data inspection/editing, calibration, imaging, self-calibration, data/image display, spectrum/time/image analysis, and production of hard copy for publication purpose. This process must be well integrated for the convenience of the user. It should be possible to easily ``mix-and-match'' self-calibration, data transformation, and de-convolution ``tools'', for example, using CLEAN to deconvolve in the early stages, and maximum entropy later on when CLEAN begins to be less useful. This is related to the need to make self-calibration use a generic model, which could be a table of CLEAN-components, a table of Gaussian components, or an image.

Image and Spectral Image Formation

1.

Image construction using u-v data sets must be possible with a range of capabilities

(a): Computation of ``dirty'' images and point spread functions by 2-D FFT of selected data with user control of data selection, gridding algorithm and its parameters, and image parameters (image size, cell sizes, polarization)
(b): Flexible computation of data cubes where the third axis is frequency/velocity or time
(c): Simultaneous, multiple field imaging
(d): Direct Fourier transform imaging of arbitrary (and usually small) size fields
(e): Imaging after subtraction for sources
(f): Imaging of spectral line data sets with continuum subtraction based upon continuum data, or continuum models
(g): Estimation and input of zero-spacing flux density and appropriate weighting

2.

Mosaic image construction using mixture of u-v data sets and single dish data for multiple antenna pointing centers

(a): Linear combination of pre-deconvolved images, weighting determined by primary beam
(b): Linear mosaic algorithm with linear deconvolution (MOSLIN in SDE)
(c): Non-linear (MEM-based) mosaic algorithm (VTESS, UTESS in AIPS, mosaic in SDE)
(d): Cross-calibration (enforced consistency) between data taken with different instruments (flux scale, pointing)
(e): Pointing self-calibration to determine corrections for both single dish and visibility data
(f): Non-coplanar baselines mosaicing allowing for sky curvature
(g): Self-calibration and editing of all pointings in one processing step
(h): Capability to determine the primary beam(s) from a mosaic image and its related data sets
(i): Ability to deal with any primary beams in different forms (analytic 1- and 2-D, tabular), including user modification of primary beam models

3.

Imaging using multiple-frequency data sets and a user-defined model for spectral combination ``rules'' must be possible

4.

Imaging computation should generally take multiple data sets where this makes sense

5.

Imaging data selection should flexibly allow use of data sub-sets, with data selection based upon time, antenna, frequency, and ranges of other data (including monitor data)

6.

Non-coplanar baselines imaging (dragon in SDE)

7.

Imaging wide fields large than the isoplanatic region

8.

Near field imaging of nearby objects like comets and asteroids

9.

Fringe-rate imaging

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