  Light microscopy is a widespread and inexpensive imaging technique
facilitating biomedical discovery and diagnostics. However, light diffraction
barrier and imperfections in optics limit the level of detail of the acquired
images. The details lost can be reconstructed among others by deep learning
models. Yet, deep learning models are prone to introduce artefacts and
hallucinations into the reconstruction. Recent state-of-the-art image synthesis
models like the denoising diffusion probabilistic models (DDPMs) are no
exception to this. We propose to address this by incorporating the physical
problem of microscopy image formation into the model's loss function. To
overcome the lack of microscopy data, we train this model with synthetic data.
We simulate the effects of the microscope optics through the theoretical point
spread function and varying the noise levels to obtain synthetic data.
Furthermore, we incorporate the physical model of a light microscope into the
reverse process of a conditioned DDPM proposing a physics-informed DDPM
(PI-DDPM). We show consistent improvement and artefact reductions when compared
to model-based methods, deep-learning regression methods and regular
conditioned DDPMs.
