Predicted Space-Varying Point Spread Function Model for Anisoplanatic Adaptive Optics Imaging

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

“…Ten frames of simulation degraded images are generated by image degradation software from the Key Optical Laboratory of the Chinese Academy of Sciences [25], and then Gaussian white noise is added and the signal-to-noise ratio (SNR) of the images is set to be 20 dB, 25 dB, and 30 dB with real AO imaging conditions including atmospheric turbulence. The equivalent parameters for the four layers turbulence model are the same as those in Reference [22], and we set the parameters to be the same as the 1.2 m AO telescope at the observatory in Yunnan, China. The main parameters of the telescope imaging system are the atmospheric coherence as shown in Table 1.…”

“…According to Zernike polynomial theory and assuming that an AO system can fully compensate for the first n order Zernike polynomials, the residual wavefront phase of isoplanatic ${\varphi }_{\mathrm{res},0}\left(z\right)$ and residual wavefront phase of anisoplanatic ${\varphi }_{\mathrm{res},\theta }\left(z\right)$ are expressed as [22] $\begin{array}{c}{\varphi }_{\mathrm{res},\theta }\left(z\right)=2\pi \left(\sum _{j=1}^n,\left(a_j\left(\theta \right)-a_j\left(0\right)\right),Z_j,\left(z\right),+,\sum _{j=n+1}^{\infty },a_j,\left(\theta \right),Z_j,\left(z\right)\right),\hfill \\ {\varphi }_{\mathrm{res},0}\left(z\right)=2\pi \sum _{j=n+1}^{\infty }{a}_j\left(0\right)Z_j\left(z\right),\hfill \end{array}$ where $a_j\left(0\right)$ and $a_j\left(\theta \right)$ represent the coefficients of the on-axis and off-axis Zernike polynomial, respectively, and $Z_j\left(z\right)$ is the j th order Zernike polynomial.…”