In modern microscopy, digital processes are decisive in many tasks, such as improving images by 3D deconvolution, achieving super-resolution or creating the ground-truth dataset required for supervised deep learning systems. The cornerstone of computational microscopy lies on knowledge of the point spread function (PSF), which sums up the entire image acquisition process. Thanks to the physical laws of light propagation and the laws of optics, we can generate synthetic yet highly realistic PSFs.

Building on our expertise gained from our PSF ImageJ plugin, PSFGenerator/Java, our goal is to provide a more precise and flexible tool. This involves integrating new modalities and transitioning to Python/PyTorch, all while maintaining a user-friendly interface.