Phase diversity wavefront sensing based on modified sparrow search algorithm

Atmospheric turbulence, optical system aberrations and other factors will cause the wavefront of the incident light wave to be distorted, thereby causing the degradation of the optical system's imaging quality. Phase diversity (PD) is an effective approach to measure these wave-front distortions. It uses two or more degraded images to estimate the wavefront aberration in the pupil plane of the imaging system . The essential of the PD is to develop an appropriate optimization algorithm to minimize the evaluation function. Traditional gradient-based nonlinear optimization algorithms, such as conjugate gradient algorithm, and quasi-Newton algorithm, are easily trapped in local minimums, which greatly limits the dynamic range of the PD method. This paper proposes a modified sparrow search algorithm (MSSA) to solve this problem. Chaotic sequences, elite opposition-based learning (EOBL) strategy and mutation operators are introduced to enhance the global search ability. The simulation results show that, this algorithm has a dynamic range of larger than 9λ PV and an accuracy of λ/100 rms, while, compared with other swarm intelligence algorithms, it has the advantages of strong search ability, fast convergence speed, and high solution accuracy. Experiments are made, which shows the effectiveness of the algorithm.