Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
On the map: Diffusion models → Inverse problems and image restoration
Abstract (arXiv)
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 (DDPMs) from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions. We provide the theoretical background, mathematical derivations, and a detailed Python code implementation using PyTorch, along with techniques to enhance model performance.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 28 of its 54 references that are in the map. First posted on arXiv 24 Sep 2024.
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28 of 54 references.
Closest ancestors · 2
Same direction, newest first
- 2024Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models6 Jun
- 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models30 Apr
Other references · 26
- 2017Attention Is All You Needcited by 59,932
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
- 2021Image Super-Resolution via Iterative Refinementcited by 1,506
- 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
14 more
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
- 2018Image Transformercited by 770
- 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
- 2019Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Modelcited by 400
- 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
- 2017FSRNet: End-to-End Learning Face Super-Resolution with Facial Priorscited by 170
- 2018Deep Learning for Single Image Super-Resolution: A Brief Reviewcited by 158
- 2020NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Resultscited by 83
- 2017Pixel Recursive Super Resolutioncited by 44
- 2023Quantum circuit synthesis with diffusion modelscited by 33
- 2023Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labelscited by 6
- 2023Conditional Variational Diffusion Modelscited by 5
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- 2024Denoising diffusion models for high-resolution microscopy image restoration
- 2023Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
- 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
- 2023PartDiff: Image Super-resolution with Partial Diffusion Models
- 2023Simple diffusion: End-to-end diffusion for high resolution images
- 2023The Missing U for Efficient Diffusion Models
- 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
- 2024Denoising Diffusion Probabilistic Models in Six Simple Steps