BLIND DECONVOLUTION USING LEAST-SQUARES MINIMIZATION

Blind deconvolution of blurred images has been demonstrated by a numbe r of authors. The quantitative performance of these algorithms is less well known, in particular the sensitivity of the reconstruction to th e inherent ambiguities in blind deconvolution and the effect of noise. We consider in detail two algorithms based on a least squares optimis ation approach. The performance of these two algorithms is also discus sed with regard to superresolving an image corrupted by an unknown blu rring function, using an example recently published in the literature [J. Opt. Soc.Am.A 11 (1194) 2401].