STRICT A-PRIORI CONSTRAINTS FOR MAXIMUM-LIKELIHOOD BLIND DECONVOLUTION

We present a maximum-likelihood approach to improve blind deconvolutio n of an image. Blind deconvolution is performed through the minimizati on of an error function by use of the conjugate gradient method, as su ggested by Lane [J. Opt. Soc. Am. A 9, 1508 (1992)]. We show how to im plement strict constraints, such as image positivity, using a reparame terization. As an example, the point-spread function can be described by phase aberrations in the case of speckle imaging. The improvement b rought by the use of strict rather than loose constraints is demonstra ted on both simulated and real data. Different noise levels and object types are considered.