SAR Bayesian SuperResolution Algorithm with a Correction of Perturbed Point Spread Function
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摘要: 推出基于贝叶斯概率公式的SAR(合成孔径雷达)图像超分辨的最大估计(EM)算法,实现SAR图像雷达截面积的重建.本算法成功地将图像场景的先验知识纳入到图像的重建过程之内,有效提高了图像的分辨力,并采用点扩展函数参数化模型,通过估计该模型的参数,抑制点扩展函数扰动的影响.二者结合,有效地实现了SAR图像的超分辨.本算法的关键是构造合理的点扩展函数模型,能够同时拟合SAR图像数据和成像系统参数的相关信息.Abstract: According to Bayesian formulation,an estimate-maximise (EM) algorithm of SAR super-resolution is presented for reconstructing radar cross sections from SAR images.The algorithm incorporates successfully the prior knowledge about the image scene into image reconstruction,which can effectively improve classical resolution of SAR image.Furthermore,the algorithm can limit the effects of perturbed point spread function (PSF) by using a parameterized model and estimating model parameters.Combining two methods,the algorithm can realize SAR super-resolution with effect.The key of the algorithm is to construct a reasonable PSF model,which can fit SAR image data and collateral SAR imaging system parameters information simultaneously.
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Key words:
- synthetic aperture radar /
- probability /
- image processing /
- super-resolution
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