基于自适应重要采样UKF的SAR图像超分辨率方法

SAR IMAGE SUPER-RESOLUTION METHOD BASED ON ADAPTIVE IMPORTANT SAMPLING UKF

针对图像中的软边缘不能完全重建导致生成图像清晰度较低的问题,提出一种基于自适应重要采样无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的SAR图像超分辨率方法。该方法利用协方差匹配技术实现自适应重要采样的UKF框架,通过将测量噪声协方差和处理噪声协方差自适应地调整到SAR图像超分辨率的强度估计框架中,恢复图像中的纹理细节。实验结果表明,当考虑观测和过程噪声协方差时,该方法的超分辨率在去噪、边缘锐化和特征保存方面的性能表现极佳。

Aiming at the problem that the soft edge of the image can not be completely reconstructed,which leads to the low definition of the generated image,a super-resolution SAR image method based on unscented kalman filter(UKF)with adaptive important sampling is proposed.This method used covariance matching technology to implement the UKF framework of adaptive important sampling,and adaptively adjusted the measured noise covariance and the processed noise covariance to the SAR image super-resolution intensity estimation framework to restore more texture details in the image.The experimental results show that when the covariance of observation and process noise is taken into account,the performance of the super-resolution method in denoising,edge sharpening and feature preservation is excellent.