Anisoplanatic deconvolution of adaptive optics images

Abstract

A modified method for maximum-likelihood deconvolution of astronomical adaptive optics images is presented. By parametrizing the anisoplanatic character of the point-spread function (PSF), a simultaneous optimization of the spatially variant PSF and the deconvolved image can be performed. In the ideal case of perfect information, it is shown that the algorithm is able to perfectly cancel the adverse effects of anisoplanatism down to the level of numerical precision. Exploring two different modes of deconvolution (using object bases of pixel values or stellar field parameters), we then quantify the performance of the algorithm in the presence of Poissonian noise for crowded and noncrowded stellar fields.

Joint deconvolution of adaptive optics images from slope measurements of wavefront sensor

“…We adopt a more secure and effective minimization method, which is to divide Eq. (15) to the minimization of turbulent phase and the minimization of object separately. First for one part, definite conjugate gradient minimization iterations are carried out, then the process is turned to the other part.…”

“…In this rule [15], when o J is approaching prior knowledge of some data, i J will also approach these data. So, there are two options for regularization rule: one is regularization function, such as o J ; the other is regularization parameter , which is used to accommodate the two variables.…”

“…Speckle interferometry [1] was first proposed and is based on recording series of images with exposure times short enough to freeze the turbulence. Various numerical post-processing methods then permit the reconstruction of the observed object [2][3][4][5][6]. Alternatively, adaptive optics (AO) [7] offers a real-time compensation for turbulence.…”

Spatially variant PSF modeling and image deblurring

“…Table 1 in Denis et al 19 ). As proposed by Flicker & Rigaut, 21 it seems then natural to decompose the PSF in a limited number of modes to form the following approximation:…”

Fast model of space-variant blurring and its application to deconvolution in astronomy

“…Smooth PSF variations can be decomposed on a subspace of PSF [8,9]. The cost of this modeling increases linearly with the number of basis PSF used.…”