遥感图像的获取、传输过程中很容易受到噪声的污染。在研究形态成分分析(MCA)稀疏分解和遥感图像 修复方法的基础上,提出了基于MCA稀疏分解的自适应去噪方法和基于图像修复的去噪方法。通过对比其他 经典去噪模型,发现前者适合自适应有效去除高斯白噪声,后者对灰度或彩色遥感图像的椒盐噪声能自适应有 效去除,且能够同时去除“胡椒”噪声和“盐”噪声,无论是主观视觉效果还是客观量化评价效果都要优于常见模型。

Remote sensing images are easily affected by noise in the process of acquisition and transmission. Based on the morphological component analysis (MCA) representation and the methods of inpainting to remote sensing images, the method of adaptive denoising on the basis of the MCA sparse decomposition and the method of denoising on the basis of image inpainting are both proposed. Compared with other classical denoising models, it is concluded that the former method can adaptivly remove the Gaussian white noise effectively, the latter method can adaptivly remove the salt and pepper noise of the gray or the colored remote sensing images effectively, especially can remove both salt noise and pepper noise at the same time. Both the subjective visual effects and the objective and quantitative evaluation of the methods are better than common models.