# Fast Multi-Level Foreground Estimation

arXiv: https://arxiv.org/abs/2006.14970 · HTML: https://mlsanity.com/paper/2006.14970/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [LayerD: Decomposing Raster Graphic Designs into Layers](https://mlsanity.com/paper/2509.25134/) (2025)
- [Matting by Generation](https://mlsanity.com/paper/2407.21017/) (2024)
- [Adversarially-Guided Portrait Matting](https://mlsanity.com/paper/2305.02981/) (2023)
- [Self-supervised Matting-specific Portrait Enhancement and Generation](https://mlsanity.com/paper/2208.06601/) (2022)
- [Improving Deep Image Matting via Local Smoothness Assumption](https://mlsanity.com/paper/2112.13809/) (2021)

## Abstract (arXiv)

Alpha matting aims to estimate the translucency of an object in a given image. The resulting alpha matte describes pixel-wise to what amount foreground and background colors contribute to the color of the composite image. While most methods in literature focus on estimating the alpha matte, the process of estimating the foreground colors given the input image and its alpha matte is often neglected, although foreground estimation is an essential part of many image editing workflows. In this work, we propose a novel method for foreground estimation given the alpha matte. We demonstrate that our fast multi-level approach yields results that are comparable with the state-of-the-art while outperforming those methods in computational runtime and memory usage.