Fast Multi-Level Foreground Estimation
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
5 papers.
- 2025LayerD: Decomposing Raster Graphic Designs into Layerscited by 20
- 2024Matting by Generationcited by 13
- 2023Adversarially-Guided Portrait Mattingcited by 4
- 2022Self-supervised Matting-specific Portrait Enhancement and Generationcited by 3
- 2021Improving Deep Image Matting via Local Smoothness Assumptioncited by 2
- 2025LayerD: Decomposing Raster Graphic Designs into Layers29 Sep 2025
- 2024Matting by Generation30 Jul 2024
- 2023Adversarially-Guided Portrait Matting4 May 2023
- 2022Self-supervised Matting-specific Portrait Enhancement and Generation13 Aug 2022
- 2021Improving Deep Image Matting via Local Smoothness Assumption27 Dec 2021