Diffusion-Shock Filtering on the Space of Positions and Orientations

TL;DR

This paper extends Regularised Diffusion-Shock filtering to the space of positions and orientations, enabling improved enhancement and inpainting of crossing structures in images by leveraging the geometry of $\,\mathbb{M}_2$.

Contribution

It introduces a novel extension of RDS filtering to $\,\mathbb{M}_2$, including gauge frame techniques, and demonstrates superior performance in denoising and inpainting crossing structures.

Findings

RDS filtering on $\,\mathbb{M}_2$ outperforms existing methods in denoising crossing structures.

$\,\mathbb{M}_2$ RDS inpainting successfully restores crossing structures.

Gauge frame approach mitigates issues from finite orientation discretization.

Abstract

We extend Regularised Diffusion-Shock (RDS) filtering from Euclidean space $\mathbb{R}^2$ to the space of positions and orientations $\mathbb{M}_2 := \mathbb{R}^2 \times S^1$. This has numerous advantages, e.g. making it possible to enhance and inpaint crossing structures, since they become disentangled when lifted to $\mathbb{M}_2$. We create a version of the algorithm using gauge frames to mitigate issues caused by lifting to a finite number of orientations. This leads us to study generalisations of diffusion, since the gauge frame diffusion is not generated by the Laplace-Beltrami operator. RDS filtering compares favourably to existing techniques such as Total Roto-Translational Variation (TR-TV) flow, NLM, and BM3D when denoising images with crossing structures, particularly if they are segmented. Additionally, we see that $\mathbb{M}_2$ RDS inpainting is indeed able to restore crossing structures, unlike $\mathbb{R}^2$ RDS inpainting.