When you click "Remove Background", GPUs perform billions of operations. We'll break down the tech stack from Pixel Calculation to Deep Learning like an algorithm engineer.
Phase 1: Chroma Key
Traditional "Green Screen" logic: Simple mathematical judgment based on color difference.
Core Algorithm
Pixel Probe
Phase 2: Deep Learning (U²-Net)
How modern AI understands complex semantics and details via "Nested U-Structure".
Semantic Segmentation
Unlike green screen, AI classifies every pixel: "This is a face" vs "This is a leaf". It distinguishes them by shape, texture, and context even if colors are similar.
Training Data
Trained on datasets like COCO, DUTS, ADE20K with tens of thousands of labeled images. It has seen portraits in thousands of lighting conditions.
Why U²-Net?
Standard networks lose detail as they get deeper. U²-Net uses a Nested U-Structure to capture both global semantics and local details (like hair strands) efficiently.
Phase 3: Alpha Matting & Unknowns
For semi-transparent edges (like hair), AI needs to solve a complex math equation.
Segmentation masks are usually binary (0 or 1). Fine for solid objects, but for hair, smoke, glass, we need an Alpha Channel (0.0 - 1.0 gray scale).
Core Concept: Trimap
The model applies expensive Matting algorithms only on "Unknown" pixels, inferring foreground opacity from surrounding pixel correlations.
Core AI Models
SOTA Saliency Detection, core variant for remove.bg
Designed for Real-time Portrait Matting, Trimap-free
Google's General Semantic Segmentation Model
Industry Workflow
Generate low-res Mask via CNN. Determine rough human outline.
Identify "Unknown Regions" (e.g., hair edges) to generate Trimap.
Solve transparency and apply Color De-spill during composition.