# Pigmento: Pigment-Based Image Analysis and Editing

arXiv: https://arxiv.org/abs/1707.08323 · HTML: https://mlsanity.com/paper/1707.08323/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Transparent Image Layer Diffusion using Latent Transparency](https://mlsanity.com/paper/2402.17113/) (2024)
- [PaletteNeRF: Palette-based Color Editing for NeRFs](https://mlsanity.com/paper/2212.12871/) (2022)
- [Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory](https://mlsanity.com/paper/2507.07333/) (2025)
- [Image Enhancement Based on Pigment Representation](https://mlsanity.com/paper/2510.02713/) (2025)

## Abstract (arXiv)

The colorful appearance of a physical painting is determined by the distribution of paint pigments across the canvas, which we model as a per-pixel mixture of a small number of pigments with multispectral absorption and scattering coefficients. We present an algorithm to efficiently recover this structure from an RGB image, yielding a plausible set of pigments and a low RGB reconstruction error. We show that under certain circumstances we are able to recover pigments that are close to ground truth, while in all cases our results are always plausible. Using our decomposition, we repose standard digital image editing operations as operations in pigment space rather than RGB, with interestingly novel results. We demonstrate tonal adjustments, selection masking, cut-copy-paste, recoloring, palette summarization, and edge enhancement.