%0 Journal Article
%T 基于OGS-HL的遥感图像混合噪声去除算法<br>Hybrid Noise Removal Algorithm of Remote Sensing Image Based on OGS-HL
%A 张阿松
%J Journal of Image and Signal Processing
%P 163-178
%@ 2325-6745
%D 2024
%I Hans Publishing
%R 10.12677/jisp.2024.132015
%X 遥感图像在成像过程中容易受到混合噪声污染，包括高斯噪声、条纹噪声和脉冲噪声等。这些混合噪声降低了遥感图像的质量，限制了其后续应用。为了解决这一问题，首先，通过对遥感图像的梯度值进行统计分析，发现遥感图像的空间梯度值是符合重尾分布，因此，设计了关于遥感图像空间梯度值的OGS-HL正则项来去除混合噪声模型，该正则项不仅可以减少全变分带来的阶梯效应，而且还可以对图像的梯度值进行合理的稀疏表示；其次，针对条纹噪声，考虑其具有低秩性且使用核范数来约束，而稀疏噪声则具有全局稀疏分布，并且采用L<sub>1</sub>范数来约束；最后，采用交替方向乘子法和Majorization-Minimization算法来求解所提出的模型。通过与现有的算法进行比较，结果表明我们提出的算法在去除高水平混合噪声方面具有良好的效果。<br />
Remote sensing images are easily contaminated by mixed noise in the imaging process, including Gaussian noise, fringe noise and impulse noise. These mixed noises degrade the quality of remote sensing images and limit their subsequent applications. In order to solve this problem, firstly, through the statistical analysis of the gradient value of the remote sensing image, it is found that the spatial gradient value of the remote sensing image conforms to the heavy-tailed distribution. Therefore, the OGS-HL regularization term about the spatial gradient value of the remote sensing image is designed to remove the mixed noise model. Moreover, the gradient value of the image can be reasonably sparse represented. Secondly, the stripe noise was considered to have low rank and was constrained by the nuclear norm, while the sparse noise had global sparse distribution and was constrained by the L<sub>1</sub> norm. Finally, the alternating direction method of multipliers and the Majorization-Minimization algorithm are used to solve the proposed model. By comparing with the existing algorithms, the results show that our proposed algorithm has a good effect in removing high-level mixed noise.
%K 遥感图像恢复，超拉普拉斯先验，重叠组稀疏性，交替方向乘子法，Majorization-Minimization<br>Remote Sensing Image Restoration
%K Hyper-Laplacian Prior
%K Overlapping Group Sparsity
%K Alternating Direction Method of Multipliers
%K Majorization-Minimization
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=84458