Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial

TL;DR

This tutorial explains how to use diffusion models, specifically DDPMs, to improve the resolution of microscopy images, including theoretical background, implementation details, and performance enhancement techniques.

Contribution

It provides a comprehensive, step-by-step guide to build and enhance diffusion-based super-resolution models for microscopy images from scratch.

Findings

Effective high-resolution microscopy image generation

Detailed Python implementation with PyTorch

Techniques for improving diffusion model performance

Abstract

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.