An extension of Joint Phase Diverse Speckle image restoration is presented. Multiple realizations of multiple objects having known wavefront relations with each other can now be restored jointly. As the alignment of the imaging setup does not change, near-perfect alignment can be achieved between different objects, thus greatly reducing false signals in the determination of derived quantities, such as magnetograms, Dopplergrams, etc. The method was implemented in C++ as an image restoration server, to which worker clients can connect and disconnect randomly, so that a large number of CPUs can be used to speed up the restorations. We present a number of examples of applications of the restoration method to observations obtained with the Swedish 1-m Solar Telescope on La Palma.

Solar Image Restoration By Use Of Multi-frame Blind De-convolution With Multiple Objects And Phase Diversity

Two new methods for modelling Kolmogorov phase fluctuations over a finite aperture are described. The first method relies on the incorporation of subharmonics in order to model accurately the low frequencies of the Kolmogorov spectrum. The second method provides a less accurate, but much faster method for simulating the Kolmogorov spectrum by using a midpoint displacement algorithm used in computer graphics.

Simulation of a Kolmogorov phase screen

The production of science-ready data from major solar telescopes requires expertise beyond that of the typical observer. This is a consequence of the increasing complexity of instruments and observing sequences, which require calibrations and corrections for instrumental and seeing effects that are not only difficult to measure, but are also coupled in ways that require careful analysis in the design of the correction procedures. Modern space-based telescopes have data-processing pipelines capable of routinely producing well-characterized data products. High resolution imaging spectropolarimeters at ground-based telescopes need similar data pipelines. We present new methods for flat-fielding spectropolarimetric data acquired with telecentric Fabry-Perot instruments and a new approach for accurate camera co-alignment for image restoration. We document a procedure that forms the basis of current state-of- the-art processing of data from the CRISP imaging spectropolarimeter at the Swedish 1 m Solar Telescope (SST). By collecting, implementing, and testing a suite of computer programs, we have defined a data reduction pipeline for this instrument. This pipeline, CRISPRED, streamlines the process of making science-ready data. It is implemented and operated in IDL, with time-consuming steps delegated to C. CRISPRED will also be the basis for the data pipeline of the forthcoming CHROMIS instrument.

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CRISPRED: A data pipeline for the CRISP imaging spectropolarimeter

An analysis of the problem of wave-front reconstruction from Shack–Hartmann measurements is presented. The wave-front aberration is assumed to result from passage of the wave front through Kolmogorov turbulence. Limitations of using Zernike polynomials as an orthogonal basis for wave-front reconstruction are highlighted, and the advantage of using the Karhunen–Loeve functions for computing the higher-order modes of the wave front is shown.

Wave-front reconstruction using a Shack-Hartmann sensor

Astronomical images obtained with adaptive optics systems can be enhanced by using image restoration techniques. However, this usually requires an accurate knowledge of the system point-spread function (PSF) which is variable in time. We present a method to estimate the PSF related to each image, using data from the adaptive optics control computer, namely, the wave-front sensor measurements and the commands to the deformable mirror, accumulated in synchronization with the acquisition. This method requires no extra observing time and has been successfully tested on PUEO, the Canada–France–Hawaii Telescope adaptive optics system. With this system, accurate PSF estimations could be achieved for guide stars of magnitude 13 or brighter.

Estimation of the adaptive optics long-exposure point-spread function using control loop data

An algorithm is described that simulates atmospherically distorted wavefronts using a Zernike expansion of randomly weighted Karhunen- Loeve functions. Its performance is presented and analyzed, and the program is then used to forecast resulting structure function and Strehl resolution for adaptive optics systems.

Nicolas A. Roddier

Papers

Atmospheric wavefront simulation using Zernike polynomials