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...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | JPhys Photonics |
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| Online Access: | https://doi.org/10.1088/2515-7647/ada101 |
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| author | Harshith Bachimanchi Giovanni Volpe |
| author_facet | Harshith Bachimanchi Giovanni Volpe |
| author_sort | Harshith Bachimanchi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dd6c91b56ef84233a3c5fade8b1c97bb |
| institution | Kabale University |
| issn | 2515-7647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | JPhys Photonics |
| spelling | doaj-art-dd6c91b56ef84233a3c5fade8b1c97bb2025-02-02T16:30:21ZengIOP PublishingJPhys Photonics2515-76472025-01-017101300110.1088/2515-7647/ada101Diffusion models for super-resolution microscopy: a tutorialHarshith Bachimanchi0https://orcid.org/0000-0001-9497-8410Giovanni Volpe1https://orcid.org/0000-0001-5057-1846Department of Physics, University of Gothenburg , Gothenburg, SwedenDepartment of Physics, University of Gothenburg , Gothenburg, SwedenDiffusion 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.https://doi.org/10.1088/2515-7647/ada101super-resolutiondiffusion modelsmicroscopy image enhancementdenoising diffusion probabilistic models (DDPMs)deep learningneural networks |
| spellingShingle | Harshith Bachimanchi Giovanni Volpe Diffusion models for super-resolution microscopy: a tutorial JPhys Photonics super-resolution diffusion models microscopy image enhancement denoising diffusion probabilistic models (DDPMs) deep learning neural networks |
| title | Diffusion models for super-resolution microscopy: a tutorial |
| title_full | Diffusion models for super-resolution microscopy: a tutorial |
| title_fullStr | Diffusion models for super-resolution microscopy: a tutorial |
| title_full_unstemmed | Diffusion models for super-resolution microscopy: a tutorial |
| title_short | Diffusion models for super-resolution microscopy: a tutorial |
| title_sort | diffusion models for super resolution microscopy a tutorial |
| topic | super-resolution diffusion models microscopy image enhancement denoising diffusion probabilistic models (DDPMs) deep learning neural networks |
| url | https://doi.org/10.1088/2515-7647/ada101 |
| work_keys_str_mv | AT harshithbachimanchi diffusionmodelsforsuperresolutionmicroscopyatutorial AT giovannivolpe diffusionmodelsforsuperresolutionmicroscopyatutorial |