Maximally sparse reconstruction of blurred star field images

The problem of removing blur from, or sharpening, astronomical star field intensity images is addressed. A new image restoration algorithm is introduced which recovers image detail using constrained optimization theoretic approach. Ideal star images may be modeled as a few point sources in a uniform background. It is therefore argued that a direct measure of image sparseness is the appropriate optimization criterion for deconvolving the image blurring function. A sparseness criterion based on the l_p quasinorm is presented, and an algorithm for sparse reconstruction is described. Synthetic and actual star image reconstruction examples are presented which demonstrate the algorithm´s superior performance as compared with the CLEAN algorithm, a standard star field deconvolution method