Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
It offers a practical resource for researchers in microscopy and related fields to apply diffusion models for image enhancement, but it is incremental as it adapts existing methods to a specific domain without claiming new breakthroughs.
This tutorial provides a comprehensive guide to building denoising diffusion probabilistic models (DDPMs) from scratch, focusing on transforming low-resolution microscopy images into high-resolution versions, including theoretical background, mathematical derivations, and Python code implementation.
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.