Deconvolution of background-subtracted shift-and-add image by a modeled point-spread-function

Shift-and-add (SAA) is a simple method for celestial speckle imaging. However, the raw SAA-image, a direct output of the SAA operations, is not useful, because a seeing-dependent huge background is superimposed on the high-resolution image of the object. To obtain the latter, a background subtraction (BGS) is applied on the raw SAA-image. The BGS-image so obtained includes negativities due to an over-subtraction that causes brightnesses of the object’s image biased downward. The negativity in the BGS-image can be removed by a deconvolution with a point-spread-function (PSF) that has negative values. In this paper, we model the PSF with a shape that possesses peak and concave portions, and perform a deconvolution by iteratively estimating the object’s image and the model parameters. The simulated experiment has shown that the present algorithm can restore the object’s image with unbiased brightnesses. Processing the observed speckle data of Io (a Jupiter satellite) by the present method has yielded a feasible Io image with reduced negativities.