On the stability of scale-space metrics

We study the stability of a classical family of metrics defined over functions' Gaussian scale-space representations, focusing on the comparison of images (functions of two variables).

These metrics have precedents both in harmonic analysis, specifically the theory of Besov spaces, and in classical methods of image processing; special cases are also known to be metrically equivalent to certain Wasserstein distances.

We quantify these metrics' robustness to geometric deformations, and introduce rotationally-invariant versions that are stable to changes in angle when comparing tomographic projections.

We also describe computationally efficient algorithms for evaluating the metrics from finite samples, and prove their robustness to additive noise.

The results are illustrated through numerical experiments.