Space object images obtained through ground-based telescopes tend to be heavily blurred and degraded by the atmospheric turbulence as well as detection noise and aberrations of optical systems. Multi-Frame Blind Deconvolution (MFBD) is currently the mainstream image restoration algorithm for images degraded by the atmospheric turbulence. MFBD jointly estimates the original image of object and the corresponding point spread functions (PSFs) from a sequence of short-exposure images. From our experience, there are still a lot of space for the improvement of the traditional MFBD algorithm. The mixed-Gaussian noise model that accounts for both the photonic and detector noise is used to replace the stationary Gaussian noise model. The L2-L1 (quadratic-linear) regularization method is used to replace originally used TV regularization method or Tikhonov regularization method. The phase annealing method is used to improve the quality of initial phase estimation and the multi-round iterative MFBD algorithm is preliminarily implemented. The simulation results demonstrate that the restored images obtained by the multi-round iterative MFBD algorithm often have better quality than that restored by traditional MFBD.
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.
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.
We demonstrate a maximum a posteriori (MAP) blind image deconvolution algorithm with bandwidth over-constrained and total variation (TV) regularization to recover a clear image from the AO corrected images. The point spread functions (PSFs) are estimated by bandwidth limited less than the cutoff frequency of the optical system. Our algorithm performs well in avoiding noise magnification. The performance is demonstrated on simulated data.
InGaAsP/InP-air-aperture micropillar cavities are proposed to serve as 1.55-μm single photon sources, which are indispensable in silica-fiber based quantum information processing. Owing to air-apertures introduced to InP layers, and adiabatically tapered distributed Bragg-reflector structures used in the central cavity layers, the pillar diameters can be less than 1 μm, achieving mode volume as small as ~(λ/n)3, and the quality factors are more than 104 - 105, sufficient to increase the quantum dot emission rate for 100 times and create strong coupling between the optical mode and the 1.55- μm InAs/InP quantum dot emitter. The mode wavelengths and quality factors are found weakly changing with the cavity size and the deviation from the ideal shape, indicating the robustness against the imperfection of the fabrication technique. The fabrication, simply epitaxial growth, dry and chemical etching, is a damage-free and monolithic process, which is advantageous over previous hybrid cavities. The above properties satisfy the requirements of efficient, photonindistinguishable and coherent 1.55-μm quantum dot single photon sources, so the proposed InGaAsP/InP-air-aperture micropillar cavities are prospective candidates for quantum information devices at telecommunication band.
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