Blind identification and restoration of the turbulence degraded images based on the nonnegativity and support constraints recursive

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.