Learning Low-Dimensional Models of Microscopes

We propose accurate and computationally efficient procedures to calibrate fluorescence microscopes from micro-beads images. The designed algorithms present many original features. First, they allow to estimate space-varying blurs, which is a critical feature for large fields of views. Second, we propose a novel approach for calibration: instead of describing an optical system through a single operator, we suggest to vary the imaging conditions (temperature, focus, active elements) to get indirect observations of its different states. Our algorithms then allow to represent the microscope responses as a low-dimensional convex set of operators. This approach is deemed as an essential step towards the effective resolution of blind inverse problems. We illustrate the potential of the methodology by designing a procedure for blind image deblurring of point sources and show a massive improvement compared to alternative deblurring approaches both on synthetic and real data.