.png){kind=icon}
Return
Diffusion models for super-resolution microscopy: a tutorial
.png){kind=icon}
OA
AI
DOI：10.1088/2515-7647/ada101.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
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
Organization
No organization information available
Cited Papers
ShareSaveShareSaveSub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM).png)
NATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAIShareSaveRationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processesNATURE BIOTECHNOLOGY
NATURE BIOTECHNOLOGYIF41.7
PREAI
ShareSaveZero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopyNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveShareSaveShareSaveSingle-frame deep-learning super-resolution microscopy for intracellular dynamics imagingNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSave
View more