结合二维EMD与自适应高斯滤波的遥感卫星影像去噪

传统的遥感影像去噪方法在去除影像噪声时，往往会造成去噪后影像细节信息丢失和模糊的问题。本文将二维EMD去噪理论用于遥感影像的去噪，提出了二维EMD与自适应高斯滤波相结合的遥感影像改进去噪算法。在去噪时能够保留低频信息不变，只对影像高频信息进行二维EMD分解后的不同频率IMF分量图作自适应高斯滤波去噪，从而更好地对含噪影像进行去噪。两组试验对比分析表明：本文算法具有较大的峰值信噪比、平均梯度和结构相似性，具有较小的均方根误差；并且边缘检测结果也表明，噪声在被滤掉的同时，经本文算法去噪后的影像能较多和更好地保留原始影像的细节信息和边缘轮廓信息，具有更好的去噪效果。

Denoising from remote sensing satellite image based on two-dimensional EMD and adaptive Gauss filtering

When traditional method of denoising from remote sensing image is used to remove image noise, it often causes the loss and blur of image details after denoising. In this paper, the two-dimensional EMD denoising theory is applied to the denoising of remote sensing images. An improved denoising algorithm for remote sensing images combined with two-dimensional EMD and adaptive Gaussian filtering is proposed. When denoising, the low-frequency information remains unchanged, only for the high-frequency information of the image. Different frequency IMF component maps after two-dimensional EMD decomposition use adaptively Gaussian filtering to denoise, so as to better denoise the noisy image. Through the comparative analysis of two groups of experiments shows that:the algorithm has larger peak signal to noise ratio, average gradient and structural similarity and smaller RMS error. And the edge detection results also show that when the noise is filtered out, the image after this algorithm denoising can be better retain the details and the edge profile information of the original image. All these show that the algorithm has better denoising effect.