Adaptive optics imaging technology for extended object based on bilateral filter

Abstract

Optimizing directly the system performance metric is an important method for correcting wave-front distortions in adaptive optics (AO) systems. In extended object imaging, some classical image quality metrics are often used as system performance evaluation function, which is the optimized object of the control algorithm. But those metrics do not consider the existence of imaging noise. Practically, the observed object images are degraded not only by the atmospheric turbulence but also imaging system noise. The noise in image will affect the value of image quality criteria and further affect the correction capability of AO system. An AO system with Stochastic Parallel Gradient Descent (SPGD) algorithm and a 61-element deformable mirror is simulated to restore the image of a turbulence-degraded extended object and the gray level variance function acted the optimized object by control algorithm. The analysis results show that the correction capability is affected severely when the turbulence strength is relatively big (D/r0<10). This paper presents a method that the observed image is processed by the bilateral filter before the system performance metric is calculated, so that the effect of noise on the system performance metric can be mitigated. Numerical simulation results verify the method is effective and the image quality based on AO technique and the bilateral filter is much better than that of only using AO technique.