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Light microscopy is a widespread and inexpensive imaging techniquefacilitating biomedical discovery and diagnostics. However, light diffractionbarrier and imperfections in optics limit the level of detail of the acquiredimages. The details lost can be reconstructed among others by deep learningmodels. Yet, deep learning models are prone to introduce artefacts andhallucinations into the reconstruction. Recent state-of-the-art image synthesismodels like the denoising diffusion probabilistic models (DDPMs) are noexception to this. We propose to address this by incorporating the physicalproblem of microscopy image formation into the model's loss function. Toovercome the lack of microscopy data, we train this model with synthetic data.We simulate the effects of the microscope optics through the theoretical pointspread function and varying the noise levels to obtain synthetic data.Furthermore, we incorporate the physical model of a light microscope into thereverse process of a conditioned DDPM proposing a physics-informed DDPM(PI-DDPM). We show consistent improvement and artefact reductions when comparedto model-based methods, deep-learning regression methods and regularconditioned DDPMs.

Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model