Wavefront-sensorless adaptive optics (AO) is commonly used to enable aberration estimation and correction using the information in images. We have introduced a machine learning (ML) approach that embeds physical understanding of the imaging process into a sensorless AO method. This enabled correction of aberrations with as few as two sample exposures across different microscope modalities. We extend the capabilities of such systems to more challenging imaging applications, including larger and more complex aberrations, lower signal levels, and specimen variations. We present a concept that permits estimation of multiple aberrations modes from a single image.
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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