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
脉冲噪声的随机性和高对比度导致其在遥感图像中难以预测和定位,为了去除脉冲噪声,本文提出了一种融合分数阶全变分先验和重叠组稀疏先验的遥感图像复原算法。该模型采用l0范数作为数据保真项以避免l1范数的过度惩罚,利用重叠组稀疏先...脉冲噪声的随机性和高对比度导致其在遥感图像中难以预测和定位,为了去除脉冲噪声,本文提出了一种融合分数阶全变分先验和重叠组稀疏先验的遥感图像复原算法。该模型采用l0范数作为数据保真项以避免l1范数的过度惩罚,利用重叠组稀疏先验来消除阶梯效应,同时分数阶全变分先验能够更有效地保留图像中的边缘和纹理信息。我们使用优化最小化算法和交替方向乘子法来进行求解,并与L0-OGSTV、HNHOTV、L0-TV三种算法进行对比,实验结果表明,本文所提出的算法在峰值信噪比和结构相似度上均优于其他几种算法。The randomness and high contrast of impulse noise cause it to be difficult to predict and localize in remote sensing images. In order to remove the impulse noise, this paper proposes a remote sensing image restoration algorithm that integrates fractional-order total variational prior and overlapping group sparse prior. The model adopts the l0 norm as the data fidelity term to avoid the over-penalization of the l1 norm, and utilizes the overlapping group sparse prior to eliminating the staircase effect, while the fractional-order total variation prior can retain the edge and texture information in the image more effectively. We use the majorization-minimization algorithm and the alternating direction multiplier method to solve the problem and compare it with the three algorithms, L0-OGSTV, HNHOTV, and L0-TV, and the experimental results show that the algorithm proposed in this paper outperforms the other algorithms in terms of the peak signal-to-noise ratio and the structural similarity.展开更多
针对在雾霾天气下获得的遥感图像产生清晰度降低,对比度和色彩保真度下降的问题,考虑到遥感图像成像较宽、信息量较大的特点,本文提出一种基于改进饱和线先验的遥感图像去雾算法。该算法首先对初始图像进行预处理,其次基于饱和线先验理...针对在雾霾天气下获得的遥感图像产生清晰度降低,对比度和色彩保真度下降的问题,考虑到遥感图像成像较宽、信息量较大的特点,本文提出一种基于改进饱和线先验的遥感图像去雾算法。该算法首先对初始图像进行预处理,其次基于饱和线先验理论对含雾图像构建饱和线以估计初始透射率,之后引入补偿因子与梯度域导向滤波器对透射率进行优化,提升了算法的鲁棒性,最后根据大气散射模型复原出清晰遥感图像。数值实验结果表明,本文算法对多种场景下的含雾遥感图像都取得了良好的效果。Aiming at the problem of reduced clarity, contrast and color fidelity of remote sensing images obtained under hazy weather, considering the characteristics of wide imaging and large amount of information in remote sensing images, we propose the remote sensing image dehazing algorithm based on the improved saturated line prior. We first preprocess the initial image, then construct saturated lines based on the saturated line prior theory for hazy image to estimate the initial transmittance, then introduce a compensation factor and the gradient-domain oriented filter to optimize the overall transmittance, which improve the robustness of the proposed algorithm, and finally recover a clear remote sensing image based on the atmospheric scattering model. Numerical experimental results show that the proposed algorithm achieves good results for hazy remote sensing images in a variety of scenes.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.
文摘脉冲噪声的随机性和高对比度导致其在遥感图像中难以预测和定位,为了去除脉冲噪声,本文提出了一种融合分数阶全变分先验和重叠组稀疏先验的遥感图像复原算法。该模型采用l0范数作为数据保真项以避免l1范数的过度惩罚,利用重叠组稀疏先验来消除阶梯效应,同时分数阶全变分先验能够更有效地保留图像中的边缘和纹理信息。我们使用优化最小化算法和交替方向乘子法来进行求解,并与L0-OGSTV、HNHOTV、L0-TV三种算法进行对比,实验结果表明,本文所提出的算法在峰值信噪比和结构相似度上均优于其他几种算法。The randomness and high contrast of impulse noise cause it to be difficult to predict and localize in remote sensing images. In order to remove the impulse noise, this paper proposes a remote sensing image restoration algorithm that integrates fractional-order total variational prior and overlapping group sparse prior. The model adopts the l0 norm as the data fidelity term to avoid the over-penalization of the l1 norm, and utilizes the overlapping group sparse prior to eliminating the staircase effect, while the fractional-order total variation prior can retain the edge and texture information in the image more effectively. We use the majorization-minimization algorithm and the alternating direction multiplier method to solve the problem and compare it with the three algorithms, L0-OGSTV, HNHOTV, and L0-TV, and the experimental results show that the algorithm proposed in this paper outperforms the other algorithms in terms of the peak signal-to-noise ratio and the structural similarity.
文摘针对在雾霾天气下获得的遥感图像产生清晰度降低,对比度和色彩保真度下降的问题,考虑到遥感图像成像较宽、信息量较大的特点,本文提出一种基于改进饱和线先验的遥感图像去雾算法。该算法首先对初始图像进行预处理,其次基于饱和线先验理论对含雾图像构建饱和线以估计初始透射率,之后引入补偿因子与梯度域导向滤波器对透射率进行优化,提升了算法的鲁棒性,最后根据大气散射模型复原出清晰遥感图像。数值实验结果表明,本文算法对多种场景下的含雾遥感图像都取得了良好的效果。Aiming at the problem of reduced clarity, contrast and color fidelity of remote sensing images obtained under hazy weather, considering the characteristics of wide imaging and large amount of information in remote sensing images, we propose the remote sensing image dehazing algorithm based on the improved saturated line prior. We first preprocess the initial image, then construct saturated lines based on the saturated line prior theory for hazy image to estimate the initial transmittance, then introduce a compensation factor and the gradient-domain oriented filter to optimize the overall transmittance, which improve the robustness of the proposed algorithm, and finally recover a clear remote sensing image based on the atmospheric scattering model. Numerical experimental results show that the proposed algorithm achieves good results for hazy remote sensing images in a variety of scenes.
