Adaptive variable selection for extended Nijboer–Zernike aberration retrieval via lasso

Abstract

In this paper, we propose extended Nijboer–Zernike (ENZ) method for aberration retrieval by incorporating lasso variable selection method which can improve the accuracy of aberration retrieval. The proposed model is computed by the state-of-art algorithm of the Bregman iterative algorithm (Bregman, 1967 [1]; Cai et al., 2008 [2]; Yin et al., 2008 [3]) for L1 minimization problem with adaptive regularized parameter choice based on the strategy (Ito et al., 2011 [4]). Numerical simulations for real world and simulated phase data validate the effectiveness of the proposed ENZ AR via lasso.