Blind restoration of noisy blurred image using a constrained maximum likelihood method

Michel Barlaud; Laure Blanc-Feraud; Pierre Mathieu

doi:10.1117/12.55828

1 April 1991 Blind restoration of noisy blurred image using a constrained maximum likelihood method

Michel Barlaud, Laure Blanc-Feraud, Pierre Mathieu

Author Affiliations +

Optical Engineering, Vol. 30, Issue 4, (April 1991). https://doi.org/10.1117/12.55828

Abstract

This paper focuses on blur estimation for image restoration. In real-life applications, blur characteristics are usually unknown, and thepoint spread function (PSF) must be determined from the degraded image. The identification and restoration procedure uses the fast Kalman filter structure. Processing on columns is decorrelated by first applying fast Fourier transform on the rows. To overcome the boundary problems, a mirror image is used. The 2-D identification and restoration problem is then transformed into a set of N 1-D identification and restoration problems on the columns. If the blur is assumed to be linear and spatially invariant, the problem is to estimate the coefficients of the PSF. This function extends over a large but limited number of pixels, thus leading to a large number of parameters that must be evaluated. The usual procedures for ARMA parameter estimation have failed in image parameter estimation, primarily due to the size of the MA part. The parameters are then constrained to be smooth by assuming that they are on a continuous function such as a Gaussian, polynomial, or wavelet function. The estimation then consists in evaluating hyperparameters computed by optimization of a likelihood function. The global procedure involves recursively estimating the unknown parameters using the maximum likelihood method, and restoring the image using the Kalman filter. Results are presented for an artificially blurred gray-level image.

Citation Download Citation

Michel Barlaud, Laure Blanc-Feraud, and Pierre Mathieu "Blind restoration of noisy blurred image using a constrained maximum likelihood method," Optical Engineering 30(4), (1 April 1991). https://doi.org/10.1117/12.55828

Published: 1 April 1991

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available