Abstract
The UNet architecture has transformed image segmentation. UNet's versatility and accuracy have driven its widespread adoption, significantly advancing fields reliant on machine learning problems with images. In this work, we gave a clear and concise mathematical explanation of UNet. We explained what is the meaning and function of each of the components of UNet. We showed that UNet was solving a control problem. We decomposed the control variables using multigrid methods. Then, operator-splitting techniques were used to solve the problem, whose architecture exactly recovered the UNet architecture. Our result showed that UNet was a one-step operator-splitting algorithm for the control problem.
| Original language | English |
|---|---|
| Pages (from-to) | 874-889 |
| Number of pages | 16 |
| Journal | Mathematical Foundations of Computing |
| Volume | 8 |
| Issue number | 5 |
| Early online date | Oct 2024 |
| DOIs | |
| Publication status | Published - Oct 2025 |
User-Defined Keywords
- UNet
- Deep neural network
- Image segmentation
- Operator splitting