Aberrations are a common problem in microscopes resulting in compromised imaging contrast and resolution. Adaptive optics (AO) can correct aberrations but requires either a wavefront sensor or a wavefront-sensorless AO method that requires multiple sample exposures.
We created a machine learning (ML) approach that embeds physical understanding of the imaging process into a sensorless AO method. This enables correction of aberrations with as few as two sample exposures. The method was translated across different microscope modalities. This includes two-photon microscopy and three-photon microscopy of in vivo mouse neural activity, showing robustness to specimen motion and activity related intensity variations.
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