A new iterative algorithm for image restoration based on maximum likelihood principle
© 1992 IEEE. This paper is concerned with the development and implementation of a new gradient-based algorithm for image restoration. The algorithm assumes that the original intensity signal s(x) has been affected by a (known) linear, but not necessarily space-invariant, point spread function (PSF) h(x,u) in an additive white Gaussian noise environment. It is assumed that the covariance function of s(x) is known a priori. Based on these assumptions, the algorithm tends toward the maximum likelihood estimate of s(x) using the steepest ascent routine. The developed algorithm is reduced to the least squares error (LSE) restoration scheme reported by Angel and Jain  in the absence of noise when the covariance function of s(x) is an impulse function. Simulation experiments are performed and presented.
Proceedings - IEEE International Symposium on Circuits and Systems
A new iterative algorithm for image restoration based on maximum likelihood principle.
Proceedings - IEEE International Symposium on Circuits and Systems,
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