# Improved Anisotropic Gaussian Filters

arXiv: https://arxiv.org/abs/2303.13278 · HTML: https://mlsanity.com/paper/2303.13278/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 41 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Discrete approximations of Gaussian smoothing and Gaussian derivatives](https://mlsanity.com/paper/2311.11317/) (2023)
- [Spatially-Variant Degradation Model for Dataset-free Super-resolution](https://mlsanity.com/paper/2407.08252/) (2024)

## Abstract (arXiv)

Elongated anisotropic Gaussian filters are used for the orientation estimation of fibers. In cases where computed tomography images are noisy, roughly resolved, and of low contrast, they are the method of choice even if being efficient only in virtual 2D slices. However, minor inaccuracies in the anisotropic Gaussian filters can carry over to the orientation estimation. Therefore, this paper proposes a modified algorithm for 2D anisotropic Gaussian filters and shows that this improves their precision. Applied to synthetic images of fiber bundles, it is more accurate and robust to noise. Finally, the effectiveness of the approach is shown by applying it to real-world images of sheet molding compounds.