Adaptive Wiener filtering with Gaussian fitted point spread function in image restoration

In the imaging process of the space remote sensing camera, there was degradation phenomenon in the acquired images. In order to reduce the image blur caused by the degradation, the remote sensing images were restored to give prominence to the characteristic objects in the images. First, the frequency-domain notch filter was adopted to remove strip noises in the images. Then using the ground characters with the knife-edge shape in the images, the point spread function of the imaging system was estimated. In order to improve the accuracy, the estimated point spread function was corrected with Gaussian fitting method. Finally, the images were restored using the adaptive Wiener filtering with the fitted point spread function. Experimental results of the real remote sensing images showed that almost all strip noises in the images were eliminated. After the denoised images were restored, its variance and its gray mean gradient increased, also its laplacian gradient increased. Restoration with Gaussian fitted point spread function is beneficial to interpreting and analyzing the remote sensing images. After restoration, the blur phenomenon of the images is reduced. The characters are highlighted, and the visual effect of the images is clearer.