基于非局部平均的多光谱遥感图像除噪声

Multispectral Remote Sensing Image Denoising Based on Non-Local Means

非局部平均除噪声的方法合理利用了图像自身的冗余性和邻域的相似性,可以获得非常好的除噪声的效果。但是目前大多关于非局部平均算法的研究主要集中在对单波段图像的除噪声方面。单独平滑多光谱遥感图像的每个波段会比较严重地损失图像的光谱特征。为此,文章提出了两方面的改进:首先,改进了非局部平均的平滑核函数,让核函数中的加权系数与每个波段建立联系而不是只涉及单一波段;其次,引入相关系数来衡量不同像素邻域的光谱相似性,并把这种光谱相似性作为非局部平均平滑约束的一部分。通过两方面的改进,传统的非局部平均的方法可以适应多光谱遥感图像的平滑除噪声。最后用不同卫星图像在不同的噪声水平下对算法进行了充分的测试,实验证明本文提出的方法更好的平滑掉了噪声而且更好的保持了图像的光谱特征。

The non-local mean denoising（NLM） exploits the fact that similar neighborhoods can occur anywhere in the image and can contribute to denoising.However,these current NLM methods do not aim at multichannel remote sensing image.Smoothing every band image separately will seriously damage the spectral information of the multispectral image.Then the authors promote the NLM from two aspects.Firstly,for multispectral image denoising,a weight value should be related to all channels but not only one channel.So for the kth band image,the authors use sum of smoothing kernel in all bands instead of one band.Secondly,for the patch whose spectral feature is similar to the spectral feature of the central patch,its weight should be larger.Bringing the two changes into the traditional non-local mean,a new multispectral non-local mean denoising method is proposed.In the experiments,different satellite images containing both urban and rural parts are used.For better evaluating the performance of the different method,ERGAS and SAM as quality index are used.And some other methods are compared with the proposed method.The proposed method shows better performance not only in ERGAS but also in SAM.Especially the spectral feature is better reserved in proposed NLM denoising.