  Invertible image representation methods (transforms) are routinely employed
as low-level image processing operations based on which feature extraction and
recognition algorithms are developed. Most transforms in current use (e.g.
Fourier, Wavelet, etc.) are linear transforms, and, by themselves, are unable
to substantially simplify the representation of image classes for
classification. Here we describe a nonlinear, invertible, low-level image
processing transform based on combining the well known Radon transform for
image data, and the 1D Cumulative Distribution Transform proposed earlier. We
describe a few of the properties of this new transform, and with both
theoretical and experimental results show that it can often render certain
problems linearly separable in transform space.
