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 estimate space-varying blurs, which is a critical feature for large fields of views. Second, we novel approach calibration: instead describing an optical system through single operator, suggest vary the imaging conditions (temperature, focus, active elements) get indirect observations its different states. Our then represent microscope responses as low-dimensional convex set operators. This deemed essential step towards effective resolution blind inverse problems. illustrate potential methodology by designing procedure image deblurring point sources show massive improvement compared alternative approaches both on synthetic real data.