Fast phase diversity wavefront sensing using object independent metrics

Phase diversity methods allow the estimation of both the wavefront disturbance and the object that is being imaged and that is extended in space. In principle, phase diversity methods can also be used for wavefront sensing without the need of wasting part of the observed light to a dedicated wavefront sensor. However, the use of phase diversity in real-time applications is prevented by its high computational complexity determined by the number of parameters quantifying the wavefront and the object. Metrics that are independent on the object have been proposed to reduce the computational complexity which allow the exclusive estimation of the wavefront. Nevertheless, these still yield a nonlinear inverse problem. To further reduce the computational complexity of the wavefront estimation methods, linear approximations of these metrics are considered that enhance the estimate of the wavefront by solving a linear least squares problem. The estimation error is studied with respect to the presence of noise and metrics presented in literature are compared.