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.
The atmospheric turbulence is a principal limitation to space objects imaging with ground-based telescopes. In order to obtain high-resolution images, post-processing is a necessary tool to overcome the effects of atmospheric turbulence. In this paper, we propose a multi-frame blind deconvolution algorithm based on the consistency constraints. We apply parametrization on the image and the PSFs, and present the minimization problem by conjugate gradient method through an alternating iterative framework. We also determine the regularization parameter adaptively at each step. Experimental results show that the proposed method can recover high quality image from turbulence degraded images effectively.
High-resolution Wavefront reconstruction using the frozen-flow hypothesis requires the wind velocities of all significant layers of turbulence in the atmosphere, which can be estimated from the time-delayed autocorrelation of the wavefront sensor (WFS) measurements. In this paper, we present a method to estimate the wind velocities of the frozen-flow atmospheric turbulence layers using the slope measurements of a Shack-Hartmann WFS. This method is tested by simulation experiments and the simulation results show that our method is efficient and the error is acceptable.
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