Simple accurate model-based phase diversity phase retrieval algorithm for wavefront sensing in high-resolution optical imaging systems

作     者：Qin, Shun Zhang, Yongbing Wang, Haoqian Chan, Wai Kin 

作者机构：Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Tsinghua Univ Grad Sch Shenzhen Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2020年第14卷第17期

页      面：4513-4519页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：China Postdoctoral Science Foundation [2020M680556] National Natural Science Foundation of China Hylink Digital Solutions Co., Ltd. Shenzhen Science and Technology Project [JCYJ20180508152042002] Guangdong Special Support [2019TX05X187] 

主　　题：optical images wavefront sensors aberrations least squares approximations Zernike polynomials nonlinear programming light diffraction image resolution high-resolution optical imaging systems high dynamic range least-squares-based nonlinear optimisation method multiple phase-diversity images model-based phase diversity phase retrieval algorithm diffraction-limited imaging wavefront sensing Lederberg-Marquardt algorithm aberration Zernike polynomials Zernike coefficients 

摘      要：In optical imaging systems, the aberration is an important factor that impedes realising diffraction-limited imaging. Accurate wavefront sensing and control play important role in modern high-resolution optical imaging systems nowadays. In this study, a simple model-based phase retrieval algorithm is proposed for accurate efficient wavefront sensing with high dynamic range. In the authors algorithm, a wavefront is represented by the Zernike polynomials, and the Zernike coefficients are solved by the least-squares-based non-linear optimisation method, i.e. the Lederberg-Marquardt algorithm, with multiple phase-diversity images. The numerical results show that the proposed algorithm is capable of retrieving wavefront with a large dynamic range up to seven wavelength and robust to noise. In comparison, the proposed algorithm is more efficient than the existing model-based technique and more accurate than existing Fourier - transformation-based iterative techniques.