It is noted, however, that the present error estimation of the pointing correction is based on assumed ideal conditions.
However, most practical computational methods used for the estimation of the point spread function are indirect and rely on some measured output of the system and possibly some additional knowledge of the scene being imaged and imaging system parameters.
As a validation test, we use both estimated point spread functions, obtained on the basis of two phantom data, and the direct estimation of the point spread function from the pinball response to deblur the natural scene pictured in Figure 4, consisting of the keyring and the coin.
The resulting estimation of the point-spread function is used in the Fourier reweighting (Fig. 1, step 4).
In short, when applied to the error in the location of one point this procedure is directly related to the uncertainty in the model relative to the position of the peak (mathematically represented by the standard error in the estimation of the point where the first derivative is zero).
In this paper, a direct method for the estimation of these points using explicit equations is introduced.
Johansson and Mast proposed a method based on an approximate estimation of the inflection point, that is, the point on the medium surface where the antenna-target raypath is bent according to Snell's law.
The amplitude data of the deblurred images, using the three estimations of the point spread function, are shown in Figure 6, and the corresponding phase images are shown in Figure 7. Figure 6 (a) Optical image of kangaroo and coin scene.
In this work, the TF mask estimation is realized through the estimation of the TF points where a signal is assumed dominant.
The implementation of EM segmentation is given as follows: The EM segmentation [46] is an unsupervised clustering method and utilized for density estimation of the data points.
