优化点扩散函数估计与稀疏约束的图像盲复原

Blind image restoration with optimization of PSF estimation and sparse constraint

在卫星光学图像盲复原问题中,点扩散函数的估计和复原模型选择对结果影响很大。针对点扩散函数模板估计困难的问题,该文在分析图像中近似点状地物成像特点的基础上,提出了基于点状地物信息的椭圆抛物面模型点扩散函数估计方法,得到较好的点扩散函数模板;针对图像噪声对复原的影响问题,采用能够分离图像的信号和噪声的归一化稀疏约束模型进行盲复原。利用"吉林一号"卫星图像进行了实验。结果表明:该文提出的优化点扩散函数估计方法可行,盲复原后图像质量得到了提高。

In the problem of blind restoration about satellite optical images, both point spread function （PSF） estimation and the selection of restored model have great influence on the results. Aiming at the problem which is difficult to estimate PSF template, by analyzing the imaging features of the approximate point feature, a PSF estimation method of elliptic paraboloid model was proposed which was based on the point feature information, a better PSF template was obtained. Aiming at the effect of noise on the image restoration, the normalized sparse representation models which could separate the image signal and noise could be used for the blind restoration. Experiments were carried out with ＂Jilin-1 mission＂ satellite ima- ges. The results show ed that the method of optimized PSF estimation was feasible, and the quality of the image was improved after blind restoration.