Diffusion models for super-resolution microscopy: a tutorial
Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models from scratch, with...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | JPhys Photonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7647/ada101 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions in the context of super-resolution microscopy. We provide the necessary theoretical background, the essential mathematical derivations, and a detailed Python code implementation using PyTorch. We discuss the metrics to quantitatively evaluate the model, illustrate the model performance at different noise levels of the input low-resolution images, and briefly discuss how to adapt the tutorial for other applications. The code provided in this tutorial is also available as a Python notebook in the supplementary information. |
|---|---|
| ISSN: | 2515-7647 |