Multiframe blind deconvolution applied to diverse shift-and-add images of an astronomical object

Abstract

In astronomical speckle imaging, deconvolving a shift-and-add (SAA) image has an advantage over deconvolving noisy specklegrams, because an SAA image is an integration of many specklegrams and has a relatively enhanced signal-tonoise ratio. In this paper, to reinforce the deconvolution of a single SAA image, we propose a multiframe deconvolution applied to multiple SAA images that are obtained by diversely recombining the same set of specklegrams to have different point spread functions. We have found that such diverse SAA images can be easily produced by permuting specklegrams to be processed by SAA. The results of experiments using simulated and observational data have shown a robustness of our present approach: in the previous approach of deconvolving a single SAA frame, the resulting object estimate is apt to be influenced by the given SAA frame and the estimation sometimes fails, whereas in the present approach, a reliable object image is stably reconstructed regardless of the given SAA frames.