In general practical applications, the point spread function (PSF) of the imaging system, the imaging process, and the
observation noise, are unknown a priori information. Therefore, the identification of the PSF is a challenging and
difficult problem in the world. The algorithm of identification of the PSF and the restoration of the blurred images based
on the priori blur models (known as the PBM algorithm) is proposed. In practical application, the priori models of the
PSF mainly consist of the linear motion blur, out of focus blur and the Gaussian blur. In the situation of that the
degradation process is formed by the one of the above point spread functions, the PSF can be formulated in parametric
model. The corresponding parameters of the model are determined by the algorithm proposed in this paper. Thus, the
PSF is obtained according to the parameter of the model consequently. First, the parameter changing scope and the
increment step length of the parameters are provided based on the original guess. Second, the criterion that the Euclid
length of the difference between the observed image and blurred image corresponding to the PSF is minimized is
incorporated in order to determine the parameter of the PSF. Therefore, the PSF is identified by the parametric model
and the original image is estimated via the ordinary image restoration algorithms. In this paper, we applied the Wiener
filter to restore the original images. The experimental results show that the identified result of the PSF is reliable and
accurate and the restoration effect with the identified PSF is better when the observed image have high signal to noise
ratio (SNR).
In general image restoration, the point spread function (PSF) of the imaging system, and the observation noise, are known a priori information. The aero-optics effect is yielded when the objects ( e.g, missile, aircraft etc.) are flying in high speed or ultrasonic speed. In this situation, the PSF and the observation noise are unknown a priori. The identification and the restoration of the turbulence degraded images is a challenging problem in the world. The algorithm based on the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) is proposed in order to identify and restore the turbulence degraded images. The NAS-RIF technique applies to situations in which the scene consists of a finite support object against a uniformly black, grey, or white background. The restoration procedure of NAS-RIF involves recursive filtering of the blurred image to minimize a convex cost function. The algorithm proposed in this paper is that the turbulence degraded image is filtered before it passes the recursive filter. The conjugate gradient minimization routine was used for minimization of the NAS-RIF cost function. The algorithm based on the NAS-RIF is used to identify and restore the wind tunnel tested images. The experimental results show that the restoration effect is improved obviously.
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