Vectorial adaptive optics

Adaptive optics normally concerns the feedback correction of phase aberrations.Such correction has been of benefit in various optical systems,with applications ranging in scale from astronomical telescopes to super-resolution microscopes.Here we extend this powerful tool into the vectorial domain,encompassing higher-dimensional feedback correction of both polarisation and phase.This technique is termed vectorial adaptive optics(V-AO).We show that V-AO can be implemented using sensor feedback,indirectly using sensorless AO,or in hybrid form combining aspects of both.We validate improvements in both vector field state and the focal quality of an optical system,through correction for commonplace vectorial aberration sources,ranging from objective lenses to biological samples.This technique pushes the boundaries of traditional scalar beam shaping by providing feedback control of extra vectorial degrees of freedom.This paves the way for next generation AO functionality by manipulating the complex vectorial field.

Universal adaptive optics for microscopy through embedded neural network control

The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens.Adaptive optics(AO)compensates these aberrations and restores diffraction limited performance.A wide range of AO solutions have been introduced,often tailored to a specific microscope type or application.Until now,a universal AO solution-one that can be readily transferred between microscope modalities-has not been deployed.We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control(MLAO)method.Unlike previous ML methods,we used a specially constructed neural network(NN)architecture,designed using physical understanding of the general microscope image formation,that was embedded in the control loop of different microscope systems.The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods,but the concept is translatable across microscope modalities.We demonstrated the method on a two-photon,a three-photon and a widefield three-dimensional(3D)structured illumination microscope.Results showed that the method outperformed commonly-used modal-based sensorless AO methods.We also showed that our ML-based method was robust in a range of challenging imaging conditions,such as 3D sample structures,specimen motion,low signal to noise ratio and activity-induced fluorescence fluctuations.Moreover,as the bespoke architecture encapsulated physical understanding of the imaging process,the internal NN configuration was no-longer a"black box",but provided physical insights on internal workings,which could influence future designs.

Revealing complex optical phenomena through vectorial metrics

Advances in vectorial polarization-resolved imaging are bringing new capabilities to applications ranging from fundamental physics through to clinical diagnosis.Imaging polarimetry requires determination of the Mueller matrix(MM)at every point,providing a complete description of an object’s vectorial properties.Despite forming a comprehensive representation,the MM does not usually provide easily interpretable information about the object’s internal structure.Certain simpler vectorial metrics are derived from subsets of the MM elements.These metrics permit extraction of signatures that provide direct indicators of hidden optical properties of complex systems,while featuring an intriguing asymmetry about what information can or cannot be inferred via these metrics.We harness such characteristics to reveal the spin Hall effect of light,infer microscopic structure within laser-written photonic waveguides,and conduct rapid pathological diagnosis through analysis of healthy and cancerous tissue.This provides new insight for the broader usage of such asymmetric inferred vectorial information.