High-resolution imaging with large ground-based telescope is challenging due to atmospheric turbulence. Adaptive optics (AO) system can provide real-time compensation for the wavefront distortion in the pupil of the telescope. However, the observed images still suffer from a blurring caused by the residual wavefront. Numerical post-processing with a good approximation of the residual wavefront can help to effectively remove the blur. In this paper, a gradients measurement model for the Shack-Hartmann wavefront sensor (WFS) in a closed-loop AO system is built. The model is based on the frozen flow hypothesis with knowledge of the wind velocities of atmospheric turbulence layers. Then a high resolution residual wavefront reconstruction method using multiframe Shack-Hartmann WFS measurements and deformable mirror voltages is presented. Numerical results show that the method can effectively improve the spatial resolution and accuracy of the reconstructed residual wavefront.
High-resolution imaging with large ground-based telescopes is limited by atmospheric turbulence. The observed images are usually blurred with unknown point spread functions (PSFs) defined in terms of the wavefront distortions of light. To effectively remove the blur, numerical postprocessing with a good approximation of the wavefront is required. The gradient measurements of the wavefront recorded by Shack–Hartmann wavefront sensor (WFS) can be used to estimate the wavefront. A gradients measurement model for Shack–Hartmann WFS is built. This model is based on the frozen flow hypothesis and uses a least-squares-fit of tip and tilt across the subaperture in the WFS to genarate the averaged gradient measurements. Then a high-resolution wavefront reconstruction method using multiframe Shack–Hartmann WFS measurements is presented. The method uses high cadence WFS data in a Bayesian framework and takes into account the available a priori information of the wavefront phase. Numerical results show that the method can effectively improve the spatial resolution and accuracy of the reconstructed wavefront in different seeing conditions.
Multi-frame blind deconvolution (MFBD) is a well-known numerical restoration technique for obtaining highresolution images of astronomical targets through the Earth’s turbulent atmosphere. The performance of MFBD algorithms depend on initial estimates for the object and the PSFs. Even though the observed image might be close to the object and could be used for the initial estimate for the object, as is often the case with the PSFs, we lack prior knowledge on the PSFs for each image. In order to provide high-quality initial estimates and improve the performance of the MFBD algorithm, one of the most effective methods is to introducing an imaging Shack-Hartmann Wave-front sensor which is similar to the traditional Shack-Hartmann Wave-front sensor but with a smaller number of lenslets across the aperture, and to process the data using a multi-channel joint restoration algorithm. In this paper, we proposed a multi-channel joint restoration algorithm which involves the usage of an imaging Shack Hartmann channel data alongside with the science camera data to improve the overall performance of the MFBD restoration algorithm. The numerical results are given in order to illustrate the performance of the joint restoration process.
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