We present a maximum-likelihood approach to improve blind deconvolution of an image. Blind deconvolution is performed through the minimization of an error function by use of the conjugate gradient method, as suggested by Lane [ J. Opt. Soc. Am. A 9, 1508 ( 1992)]. We show how to implement strict constraints, such as image positivity, using a reparameterization. As an example, the point-spread function can be described by phase aberrations in the case of speckle imaging. The improvement brought by the use of strict rather than loose constraints is demonstrated on both simulated and real data. Different noise levels and object types are considered.
© 1995 Optical Society of America
Original Manuscript: January 19, 1994
Revised Manuscript: August 22, 1994
Manuscript Accepted: September 12, 1994
Published: March 1, 1995
E. Thiébaut and J.-M. Conan, "Strict a priori constraints for maximum-likelihood blind deconvolution," J. Opt. Soc. Am. A 12, 485-492 (1995)