Chromatic aberration correction employing reinforcement learning.

In fluorescence microscopy a multitude of labels are used that bind to different structures of biological samples. These often require excitation at different wavelengths and lead to different emission wavelengths. The presence of different wavelengths can induce chromatic aberrations, both in the optical system and induced by the sample. These lead to a detuning of the optical system, as the focal positions shift in a wavelength dependent manner and finally to a decrease in the spatial resolution. We present the correction of chromatic aberrations by using an electrical tunable achromatic lens driven by reinforcement learning. The tunable achromatic lens consists of two lens chambers filled with different optical oils and sealed with deformable glass membranes. By deforming the membranes of both chambers in a targeted manner, the chromatic aberrations present in the system can be manipulated to tackle both systematic and sample induced aberrations. We demonstrate chromatic aberration correction of up to 2200 mm and shift of the focal spot positions of 4000 mm. For control of this non-linear system with four input voltages, several reinforcement learning agents are trained and compared. The experimental results show that the trained agent can correct system and sample induced aberration and thereby improve the imaging quality, this is demonstrated using biomedical samples. In this case human thyroid was used for demonstration.