点扩散函数高斯拟合估计与遥感图像恢复

摘要

In order to reduce or eliminate the image blur caused by the degradation in imaging process of a space remote sensing camera,the acquired images were restored to enhance the characteristic objects of images.First,a frequency-domain notch filter was adopted to remove noises in the remote sensing images.Then,the degradation function,namely,Point Spread Function（PSF） of the imaging system was estimated by ground characters with the knife-edge shape,and the estimated PSF was corrected with Gaussian fitting.Finally,the images were restored by using adaptive Wiener filtering with the fitted PSF.Experimental results show that almost all strip noises in the image are eliminated.After the denoised image is restored,its variance and gray mean gradient increase by 4.395 and 1.799,respectively,and also Laplacian gradient increases by 10.014.Experiments demonstrate that the restoration with Gaussian fitted PSF 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 effects of the images are clearer.%为了减轻或消除航天遥感相机成像过程中图像退化造成的模糊,突出图像的特征目标,对获取的图像进行了恢复处理。首先,采用陷波滤波器在频率域对遥感图像进行了去噪预处理。然后,通过图像中具有刀刃边缘的地物估计成像系统的退化函数,即点扩散函数;同时,利用高斯拟合对估计的点扩散函数进行校正。最后,利用拟合后的点扩散函数,采用自适应维纳滤波对图像进行恢复。实验结果表明：陷波滤波器基本消除了图像中叠加的条带噪声。与原图相比,细节图像恢复后其方差增大4.395,灰度平均梯度增大1.799,Laplacian梯度增大10.014,图像目视效果更清晰。高斯拟合的点扩散函数用于遥感图像恢复,减轻了图像模糊,使图像细节突出,纹理清晰,利于图像的判读和分析。