Open Access Open Access  Restricted Access Subscription or Fee Access

Recovering Images, Registered by Device with Inexact Point-Spread Function, Using Tikhonov’s Regularized Least Squares Method

Vladimir Erokhin, Vladimir Volkov

Abstract


The theoretical and practical aspects of using Tikhonov’s regularized least squares method to solve the image restoration problem are considered. We suppose, that degraded image was obtained from registering device with inexact point-spread function. The method of reducing image restoration problem to problem of solving approximate systems of linear algebraic equations and some matrix transformation of this systems are discussed. Both positive and negative values of regularization parameter are considered, which is not common practice in Tikhonov regularization. The results of computational experiments are
given. This experiments shows that there are such conditions that leads to necessity of using negative regularization parameter in Tikhonov’s regularized least squares method.

Keywords


regularized least squares method, regularization, image restoration, the approximate system of linear algebraic equations.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.

thentic information.