This microtubule does not exist: Super-resolution microscopy image generation by a diffusion model

Abstract

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, we explore the adaptation of a diffusion model to super-resolution microscopy images and train it on images from a publicly available database. We show that the generated images resemble experimental images, and that the generated images do not copy existing images from the training set. Additionally, we compare the performance of a deep-learning-based deconvolution method when trained on our generated data versus training on mathematical model based data and show superior reconstruction quality in means of spatial resolution. These findings demonstrate the potential contribution of generative diffusion models for microscopy tasks and pave the way for their future application in this field.