Abstract

We present a new projection-based algorithm for solving the classical blind-deconvolution problem. In our approach all known a priori information about both the unknown source and the blurring functions is expressed through constraint sets. In computer simulations the algorithm performed well even when the prior information was not accurate. To see how well our algorithm compares against others, we compared it with another recently published deconvolution method [ J. Opt. Soc. Am. A 9, 932 ( 1992)]. The advantages of each method are discussed.

© 1994 Optical Society of America

Blind deconvolution of speckle images

R. G. Lane
J. Opt. Soc. Am. A 9(9) 1508-1514 (1992)

Strict a priori constraints for maximum-likelihood blind deconvolution

E. Thiébaut and J.-M. Conan
J. Opt. Soc. Am. A 12(3) 485-492 (1995)

Convex optimization-based blind deconvolution for images taken with coherent illumination

Reinier Doelman and Michel Verhaegen
J. Opt. Soc. Am. A 36(4) 678-685 (2019)

Iterative statistical approach to blind image deconvolution

Edmund Y. Lam and Joseph W. Goodman
J. Opt. Soc. Am. A 17(7) 1177-1184 (2000)

Blind multi-frame deconvolution by tangential iterative projections (TIP)

Dean Wilding, Oleg Soloviev, Paolo Pozzi, Gleb Vdovin, and Michel Verhaegen
Opt. Express 25(26) 32305-32322 (2017)