# Sliced L2 Distance for Colour Grading

arXiv: https://arxiv.org/abs/2102.09297 · HTML: https://mlsanity.com/paper/2102.09297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Regularized Discrete Optimal Transport](https://mlsanity.com/paper/1307.5551/) (2013)
- [Discriminator optimal transport](https://mlsanity.com/paper/1910.06832/) (2019)
- [Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections](https://mlsanity.com/paper/1905.10099/) (2019)
- [Shape Registration with Directional Data](https://mlsanity.com/paper/1708.07791/) (2017)
- [Patch based Colour Transfer using SIFT Flow](https://mlsanity.com/paper/2005.09015/) (2020)
- [Iterative Nadaraya-Watson Distribution Transfer for Colour Grading](https://mlsanity.com/paper/2006.09208/) (2020)

## Abstract (arXiv)

We propose a new method with L2 distance that maps one N-dimensional distribution to another, taking into account available information about correspondences. We solve the high-dimensional problem in 1D space using an iterative projection approach. To show the potentials of this mapping, we apply it to colour transfer between two images that exhibit overlapped scenes. Experiments show quantitative and qualitative competitive results as compared with the state of the art colour transfer methods.