遥感图像自适应去噪方法研究  

Investigation on Adaptive Denoising of Remote Sensing Image

作　　者：张继尧[1] 张渫 刘晓[1,3] 易维宁[1] 

机构地区：[1]中国科学院安徽光学精密机械研究所中国科学院通用光学定标与表征技术重点实验室,安徽合肥230031 [2]解放军总后建筑工程研究所,陕西西安710032 [3]电子工程学院脉冲功率激光技术国家重点实验室,安徽合肥230037

出　　处：《大气与环境光学学报》2011年第5期368-376,共9页Journal of Atmospheric and Environmental Optics

基　　金：国家自然科学基金(41071232)资助

摘　　要：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 inpalnting 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 axe 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.

关 键 词：遥感图像 稀疏分解 图像修复 图像去噪 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]