# Total Directional Variation for Video Denoising

## TL;DR

This paper introduces a variational video denoising method using total directional variation (TDV) regularisation, which leverages anisotropic structure encoding via a volumetric structure tensor to improve denoising performance.

## Contribution

The paper extends the TDV regulariser to video denoising by incorporating a volumetric structure tensor, enhancing the preservation of anisotropic features in videos.

## Key findings

- Outperforms some state-of-the-art video denoising methods
- Effectively captures anisotropic structures in videos
- Demonstrates improved denoising quality in numerical experiments

## Abstract

In this paper, we propose a variational approach for video denoising, based on a total directional variation (TDV) regulariser proposed in Parisotto et al. (2018), for image denoising and interpolation. In the TDV regulariser, the underlying image structure is encoded by means of weighted derivatives so as to enhance the anisotropic structures in images, e.g. stripes or curves with a dominant local directionality. For the extension of TDV to video denoising, the space-time structure is captured by the volumetric structure tensor guiding the smoothing process. We discuss this and present our whole video denoising work-flow. Our numerical results are compared with some state-of-the-art video denoising methods.