# Sequential edge detection using joint hierarchical Bayesian learning

arXiv: https://arxiv.org/abs/2302.14247 · HTML: https://mlsanity.com/paper/2302.14247/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 74 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Automated Parameter Selection for Total Variation Minimization in Image Restoration](https://mlsanity.com/paper/1509.07442/) (2015)
- [Generalized sparse Bayesian learning and application to image reconstruction](https://mlsanity.com/paper/2201.07061/) (2022)
- [Sequential image recovery using joint hierarchical Bayesian learning](https://mlsanity.com/paper/2206.12745/) (2022)
- [Edge-adaptive l2 regularization image reconstruction from non-uniform Fourier data](https://mlsanity.com/paper/1811.08487/) (2018)

## Built on by (most cited first, 1 of 1)

- [Leveraging joint sparsity in hierarchical Bayesian learning](https://mlsanity.com/paper/2303.16954/) (2023)

## Abstract (arXiv)

This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new method is cast in a Bayesian framework and uses a prior that simultaneously incorporates intra-image information to promote sparsity in each individual edge map with inter-image information to promote similarities in any unchanged regions. By treating both the edges as well as the similarity between adjacent images as random variables, there is no need to separately form regions of change. Thus we avoid both additional computational cost as well as any information loss resulting from pre-processing the image. Our numerical examples demonstrate that our new method compares favorably with more standard SBL approaches.