Use of the single-image approach in multiple-image deblurring problems with Poissonian noise

Abstract

It has been shown that the restoration of a set of images of a fixed object, blurred by different space-invariant point spread functions (PFS) and contaminated by additive Gaussian noise, can be reformulated as a standard single-image least-squares (LS) deblurring problem with remarkable advantages that concern both the computational burden as well as the storage memory. The only difficulty is represented by the fact that the resulting single-image can have negative values. In a recent paper by Anconelli et al. (2005, A&A, 430, 731) this approach has been coupled with the standard Richardson-Lucy (RL) algorithm. However, their way of dealing with negative values of the single-image is not the most appropriate one. In this note we propose a different method which provides better reconstructions and convergence rates.