![]() The bound on the expected time until extinction depends asymptotically linearly on the populations size Σ. Alternatively, multi-scale techniques could be used, but no “ready-to-go” or “best” approach has been agreed upon so far.įig. In general, maximum likelihood blur identification procedures require good initializations of the parameters to be estimated in order to ensure converge to the global optimum. This makes the optimization of (35) difficult, no matter what optimization procedure is used. Secondly, the log-likelihood function (35) is highly nonlinear and has many local maxima. Symmetry of the point-spread function of the blur, i.e., d(-n 1, - n 2) = d( n 1, n 2). The energy conservation principle, as described by equation (5b), In the first place, some constraints must be enforced in order to obtain a unique estimate for the point-spread function. ![]() Actually the differentiation between state-of-the-art blur identification procedures is mostly in the way they handle these problems. The maximum likelihood estimation approach has several problems that require non-trivial solutions. The parameters of this image model - that is, the prediction coefficients a i,j and the variance σ 2 v of the white noise v( n 1, n 2) - are not necessarily assumed to be known. Most maximum likelihood identification techniques begin by assuming that the ideal image can described with the 2D auto-regressive model (20a). Maximum likelihood estimation is a well-known technique for parameter estimation in situations where no stochastic knowledge is available about the parameters to be estimated. To this end maximum likelihood estimation procedures for the unknown coefficients have been developed. In case the point-spread function does not have characteristic spectral zeros or in case a parametric blur model such as motion or out-of-focus blur cannot be assumed, the individual coefficients of the point-spread function have to be estimated. Lagendijk, Jan Biemond, in Handbook of Image and Video Processing (Second Edition), 2005 4.2 Maximum Likelihood Blur Estimation ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |