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Diffusion models for super-resolution microscopy: a tutorial
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DOI：10.1088/2515-7647/ada101.png)
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Abstract
En 中文
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.
Keywords:
super-resolution
diffusion models
microscopy image enhancement
denoising diffusion probabilistic models (DDPMs)
deep learning
neural networks
python tutorial
Journal
J
IF:
8.4
Papers:
125
Citations:
1.4K
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