  We demonstrate the use of an eigenbasis that is derived from principal
component analysis (PCA) applied on an ensemble of random-noise images that
have a "red" power spectrum; i.e., a spectrum that decreases smoothly from
large to small spatial scales. The pattern of the resulting eigenbasis allows
for the reconstruction of images with a broad range of image morphologies. In
particular, we show that this general eigen basis can be used to efficiently
reconstruct images that resemble possible astronomical sources for
interferometric observations; even though the images in the original ensemble
used to generate the PCA basis are significantly different from the
astronomical images. We further show that the efficiency and fidelity of the
image reconstructions depends only weakly on the particular parameters of the
red-noise power spectrum used to generate the ensemble of images.
