Robust statistical phase-diversity method for high-accuracy wavefront sensing

Phase diversity phase retrieval (PDPR) has been a popular technique for quantitatively measuring wavefront errors of optical imaging systems by extracting the information from several designated intensity measurements. As problem is inverse and non-convex in general, accuracy robustness most such algorithms rely greatly on initial conditions. In this work, we propose new strategy that combines Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) with points generated k-means clustering method three various channels to improve overall performance. Experimental results show that, 500 different aberrations root mean square (RMS) value bounded within [0.2?, 0.3?], minimum, maximum RMS residual reach 0.017?, 0.066? 0.039?, respectively, 84.8% are less than 0.05?. We have further investigated analyzed proposed details demonstrate its performance: statistical reveal our PDPR enhanced excellent terms other influential factors, can outperform counterpart methods as classic L-BFGS modified BFGS.