| edge | stsdas.playpen | edge |
edge -- 2-D image edge massaging.
edge input output
Several image restoration methods rely on an underlying assumption of circular convolution. However, actual convolutions that take place in an imaging system are linear instead. This difference results in artifacts associated with the image edges. These artifacts may be overcome by properly processing the image edges before submitting the image to the restoration procedure.
This task modifies image edges by applying either of two techniques:
(i) Windowing. (ii) Overlapped edge extension.Windowing is the multiplication of the data frame by a tapering function. This function has value 1 over all the image except at a region near the edges, where it smoothly decays to zero. The resulting image will be closer to the one produced by circular convolution. The procedure is quite fast, but has the drawback of reducing the image's information content. The task includes Parzen (linear), Welch (quadratic) and Hanning (cosine-bell) functional forms.
A better approach is to perform an overlapped edge extension. The data frame edges are extended beyond the data limits, generating a larger pixel array than the input one. The extended region is filled up with interpolated values from the image itself, in such a way as to make the resulting image approximately circulant for the specific convolution at hand. The size of the extended region must be equal to half the PSF support size. The details are explained in Bates & McDonnell ("Image Restoration and Reconstruction", 1986, Claredon Press, p.~63, see also Image Restoration Newslleter, Space Telescope Science Institute, Summer 1993, p. 38).
Images of different sizes can be input simultaneously to the task.
This task was written by I.Busko