# Total Variation Applications in Computer Vision

arXiv: https://arxiv.org/abs/1603.09599 · HTML: https://mlsanity.com/paper/1603.09599/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 6 of 6)

- [Data Leakage in Federated Averaging](https://mlsanity.com/paper/2206.12395/) (2022)
- [SEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics](https://mlsanity.com/paper/2309.10972/) (2023)
- [Adversarial Image Generation by Spatial Transformation in Perceptual Colorspaces](https://mlsanity.com/paper/2310.13950/) (2023)
- [Data-Efficient Limited-Angle CT Using Deep Priors and Regularization](https://mlsanity.com/paper/2502.12293/) (2025)
- [Compressive Image Scanning Microscope](https://mlsanity.com/paper/2307.09841/) (2023)
- [Overlap-aware segmentation for topological reconstruction of obscured objects](https://mlsanity.com/paper/2510.06194/) (2025)

## Abstract (arXiv)

The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter λ controls the amount of regularization allowed and it is an essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed.