为了去除相机拍摄的遥感图像中的云雾，提出了一种新的非局域均值算法来处理遥感图像序列中的云雾噪声。首先，根据遥感图像在云雾阴影下的梯度值的性质，得出了在云雾阴影下图像灰度值偏低而梯度值却变化不大的结论，从而在权重值的计算中耦合了梯度值信息。然后，利用序列图像的帧间冗余信息来计算新的权重值。最后，用新的权重值对图像进行恢复。用UltraCamD相机对在我国新疆地区和山西地区上空拍摄的两组遥感图像序列进行的实验表明:与传统的图像恢复算法相比，用提出的方法恢复图像可获得更好的图像质量；与原始图像相比，恢复后图像的峰值信噪比提高了9 dB以上。实验结果表明，该算法可以在不知道云层运动方向和相机运动方向以及噪声模型的情况下有效地对薄云雾覆盖的遥感图像进行恢复。

In order to eliminate the cloud and mist from remote sensing images captured by cameras, a new non-local means algorithm is proposed to process the cloud and mist noise in remote sensing images. Based on the gradient feature under the shadow of cloud and mist in the remote sensing images, it is found that the intensity of the image declines obviously while the gradient only has a little change,therefore, the gradient information can be coupled into the weight computation. Then, the redundant information in image sequences is used to compute the new weights and the new weights are used to restore the image sequences. Two remote sensing image sequences are taken by UltraCamD in Xinjiang and Shanxi in China, and results show that the quality of restored image is improved significantly by this algorithm. Compared with the original images,the PSNR by proposed method has improved by more than 9 dB. Experiments show that the proposed algorithm can effectively restore remote sensing images without the motion estimation for the cloud and camera as well as the noise model.