Image Background Removal Research Summary
This article provides an overview of the research behind these advances. Understanding the evolution of matting can be helpful when working with background removal in various applications.
When we talk about background removal, two techniques often come up: image segmentation and image matting. While they seem similar at a glance, they solve different problems.
Segmentation vs. Matting
- Segmentation assigns a hard label (foreground or background) to each pixel.
- Matting, on the other hand, estimates an alpha value (0 to 1) per pixel, representing how much a pixel belongs to the foreground. This is crucial for semi-transparent or soft edges like hair, smoke, or glass.
Segmentation | Image Matting |
---|---|
Hard edges, binary mask | Soft transitions, alpha matte |
This difference makes matting the go-to solution when quality matters especially in photography, film, and advanced background removal workflows.
Human-in-the-Loop vs. Fully Automatic Approaches
Many matting methods require guidance from the user. This guidance is commonly in the form of a trimap. This trimap defines:
- Definite foreground (white)
- Definite background (black)
- Unknown area (gray), where the model must estimate the alpha matte

While trimap-based models like DIM or IndexNet still lead in quality, they rely on user input—not ideal for large-scale or real-time scenarios.
Recent advances aim for fully automated matting, where no user annotation is needed. These "trimap-free" models (e.g., MODNet, MatteFormer) are pushing the boundaries of real-time background removal.