# A Robust Variational Model for Positive Image Deconvolution

arXiv: https://arxiv.org/abs/1310.2085 · HTML: https://mlsanity.com/paper/1310.2085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2013.

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

- [On Scale Space Radon Transform, Properties and Application in CT Image Reconstruction](https://mlsanity.com/paper/2205.05188/) (2022)

## Abstract (arXiv)

In this paper, an iterative method for robust deconvolution with positivity constraints is discussed. It is based on the known variational interpretation of the Richardson-Lucy iterative deconvolution as fixed-point iteration for the minimisation of an information divergence functional under a multiplicative perturbation model. The asymmetric penaliser function involved in this functional is then modified into a robust penaliser, and complemented with a regulariser. The resulting functional gives rise to a fixed point iteration that we call robust and regularised Richardson-Lucy deconvolution. It achieves an image restoration quality comparable to state-of-the-art robust variational deconvolution with a computational efficiency similar to that of the original Richardson-Lucy method. Experiments on synthetic and real-world image data demonstrate the performance of the proposed method.