Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
<|reference_start|>Diffusion Models to Enhance the Resolution of Microscopy Images: 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 (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.<|reference_end|>
@article{bachimanchi2024diffusion,
    title={Diffusion Models to Enhance the Resolution of Microscopy Images: A
  Tutorial},
    author={Harshith Bachimanchi and Giovanni Volpe},
    journal={arXiv preprint arXiv:2409.16488},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16488},
    primaryClass={eess.IV cs.CV cs.LG q-bio.OT}
}