遥感图像在采集时,由于受到各种因素干扰,会严重影响图像的视觉效果,进而影响后续处理的准确性。因此,对遥感图像的噪声进行准确地建模是解决遥感图像噪声问题的关键。编码噪声分布最常选择是高斯分布、拉普拉斯分布和高斯混合分布,但...遥感图像在采集时,由于受到各种因素干扰,会严重影响图像的视觉效果,进而影响后续处理的准确性。因此,对遥感图像的噪声进行准确地建模是解决遥感图像噪声问题的关键。编码噪声分布最常选择是高斯分布、拉普拉斯分布和高斯混合分布,但它们总是与现实世界的遥感图像噪声不相容。考虑到遥感图像同时存在对称和非对称的噪声分布,本文在高斯混合分布基础上,引入了非对称参数,构建了一个基于非对称高斯混合分布模型(AMoG)的遥感图像去噪算法。该算法使用低秩矩阵分解将遥感图像近似为两个因子矩阵的乘积。对于模型的参数,使用了EM算法进行迭代更新。在合成数据集和真实数据集上的大量实验结果表明,该模型在PSNR、SSIM、FSIM、ERGA、SAM五种评价指标上均表现良好,表明了该算法在遥感图像去噪方面具有一定的优越性。Remote sensing images are often subject to various interferences during acquisition, which seriously affects the visual effect of the images, and then affects the accuracy of subsequent processing. Therefore, accurate modeling of the noise of remote sensing images is the key to solving the noise problem of remote sensing images. The most common choices for coded noise distributions are Gaussian, Laplace, and Gaussian mixtures, but they are always incompatible with real-world remote sensing image noise. Considering that there are both symmetrical and asymmetric noise distribution in remote sensing images, this paper introduces asymmetric parameters on the basis of Gaussian mixed distribution, and constructs a remote sensing image denoising algorithm based on asymmetric Gaussian mixed distribution model (AMoG). The algorithm uses low-rank matrix factorization to approximate the remote sensing image as the product of two-factor matrices. For the parameters of the model, the EM algorithm was used for iterative update. A large number of experimental results on synthetic datasets and real datasets show that the model performs well in five evaluation indexes: PSNR, SSIM, FSIM, ERGA and SAM, indicating that the algorithm has certain advantages in remote sensing image denoising.展开更多
文摘遥感图像在采集时,由于受到各种因素干扰,会严重影响图像的视觉效果,进而影响后续处理的准确性。因此,对遥感图像的噪声进行准确地建模是解决遥感图像噪声问题的关键。编码噪声分布最常选择是高斯分布、拉普拉斯分布和高斯混合分布,但它们总是与现实世界的遥感图像噪声不相容。考虑到遥感图像同时存在对称和非对称的噪声分布,本文在高斯混合分布基础上,引入了非对称参数,构建了一个基于非对称高斯混合分布模型(AMoG)的遥感图像去噪算法。该算法使用低秩矩阵分解将遥感图像近似为两个因子矩阵的乘积。对于模型的参数,使用了EM算法进行迭代更新。在合成数据集和真实数据集上的大量实验结果表明,该模型在PSNR、SSIM、FSIM、ERGA、SAM五种评价指标上均表现良好,表明了该算法在遥感图像去噪方面具有一定的优越性。Remote sensing images are often subject to various interferences during acquisition, which seriously affects the visual effect of the images, and then affects the accuracy of subsequent processing. Therefore, accurate modeling of the noise of remote sensing images is the key to solving the noise problem of remote sensing images. The most common choices for coded noise distributions are Gaussian, Laplace, and Gaussian mixtures, but they are always incompatible with real-world remote sensing image noise. Considering that there are both symmetrical and asymmetric noise distribution in remote sensing images, this paper introduces asymmetric parameters on the basis of Gaussian mixed distribution, and constructs a remote sensing image denoising algorithm based on asymmetric Gaussian mixed distribution model (AMoG). The algorithm uses low-rank matrix factorization to approximate the remote sensing image as the product of two-factor matrices. For the parameters of the model, the EM algorithm was used for iterative update. A large number of experimental results on synthetic datasets and real datasets show that the model performs well in five evaluation indexes: PSNR, SSIM, FSIM, ERGA and SAM, indicating that the algorithm has certain advantages in remote sensing image denoising.
