# Progressive Color Transfer with Dense Semantic Correspondences

arXiv: https://arxiv.org/abs/1710.00756 · HTML: https://mlsanity.com/paper/1710.00756/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

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

- [Image Inpainting with External-internal Learning and Monochromic Bottleneck](https://mlsanity.com/paper/2104.09068/) (2021)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [Cross-Camera Deep Colorization](https://mlsanity.com/paper/2209.01211/) (2022)
- [Image color correction, enhancement, and editing](https://mlsanity.com/paper/2107.13117/) (2021)
- [Reference Based Color Transfer for Medical Volume Rendering](https://mlsanity.com/paper/2210.08083/) (2022)
- [Weak Segmentation-Guided GAN for~Realistic Color Edition](https://mlsanity.com/paper/2610.16620/) (2026)

## Abstract (arXiv)

We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semantically-meaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially variant and globally coherent. Therefore, our algorithm optimizes a local linear model for color transfer satisfying both local and global constraints. Our proposed approach jointly optimizes matching and color transfer, adopting a coarse-to-fine strategy. The proposed method can be successfully extended from one-to-one to one-to-many color transfer. The latter further addresses the problem of mismatching elements of the input image. We validate our proposed method by testing it on a large variety of image content.