Deep Learning Assisted Zonal Adaptive Aberration Correction

Deep learning (DL) has been recently applied to adaptive optics (AO) to correct optical aberrations rapidly in biomedical imaging. Here we propose a DL assisted zonal adaptive correction method to perform corrections of high degrees of freedom while maintaining the fast speed. With a trained DL neural network, the pattern on the correction device which is divided into multiple zone phase elements can be directly inferred from the aberration distorted point-spread function image in this method. The inference can be completed in 12.6 ms with the average mean square error 0.88 when 224 zones are used. The results show a good performance on aberrations of different complexities. Since no extra device is required, this method has potentials in deep tissue imaging and large volume imaging.

Automated summary

The proposed method utilizes deep learning neural networks to rapidly correct optical aberrations, achieving high degrees of freedom corrections while maintaining fast speed. Experimental results demonstrate good performance in correcting aberrations of different complexities, and the method has potential applications in deep tissue imaging and large volume imaging without the need for extra devices.