Learning-based lens wavefront aberration recovery

Abstract

Wavefront aberration describes the deviation of a wavefront in an imaging system from a desired perfect shape, such as a plane or a sphere, which may be caused by a variety of factors, such as imperfections in optical equipment, atmospheric turbulence, and the physical properties of imaging subjects and medium. Measuring the wavefront aberration of an imaging system is a crucial part of modern optics and optical engineering, with a variety of applications such as adaptive optics, optical testing, microscopy, laser system design, and ophthalmology. While there are dedicated wavefront sensors that aim to measure the phase of light, they often exhibit some drawbacks, such as higher cost and limited spatial resolution compared to regular intensity measurement. In this paper, we introduce a lightweight and practical learning-based method, named LWNet, to recover the wavefront aberration for an imaging system from a single intensity measurement. Specifically, LWNet takes a measured point spread function (PSF) as input and recovers the wavefront aberration with a two-stage network. The first stage network estimates an initial wavefront aberration via supervised learning, and the second stage network further optimizes the wavefront aberration via self-supervised learning by enforcing the statistical priors and physical constraints of wavefront aberrations via Zernike decomposition. For supervised learning, we created a synthetic PSF-wavefront aberration dataset via ray tracing of 88 lenses. Experimental results show that even trained with simulated data, LWNet works well for wavefront aberration estimation of real imaging systems and consistently outperforms prior learning-based methods.