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, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few e

SUBMITTER: Saguy A 

PROVIDER: S-EPMC11926487 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature