遥感图像自适应去噪方法研究

大气与环境光学学报 ›› 2011, Vol. 6 ›› Issue (5): 368-376.

遥感图像自适应去噪方法研究

张继尧¹, 张渫², 刘晓^1,3, 易维宁¹

（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）

收稿日期:2011-04-11 修回日期:2011-04-17 发布日期:2011-09-09
通讯作者: 易维宁（1956-），女，研究员，主要从事地物波谱特征、大气辐射校正和遥感表征模型方面的研究工作。 Email: yiwn@aiofm.ac.cn E-mail:zjy2008@mail.ustc.edu.cn
作者简介:张继尧（1984-），男，云南龙陵人，研究生，主要从事遥感图像处理。Email: zjy2008@mail.ustc.edu.cn
基金资助:
国家自然科学基金（41071232）资助

Investigation on Adaptive Denoising of Remote Sensing Image

ZHANG Ji-yao¹, ZHANG Xie², LIU Xiao^1,3, YI Wei-ning¹

（1 Key Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, of Chinese Academy of Sciences, Hefei 230031, China;
2 Construction Engineering Research Institute of the General Logistics Department of the PLA, Xi’an 710032, China;
3 State Key Laboratory of Pulsed Laser Technology, Electronic Engineering Institute, Hefei 230037, China）

Received:2011-04-11 Revised:2011-04-17 Online:2011-09-09

摘要/Abstract

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。

关键词: 遥感图像, 稀疏分解, 图像修复, 图像去噪

Abstract:

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.

Key words: remote sensing images, sparse decomposition, image inpainting, image denoising

中图分类号:

TP391

张继尧张渫刘晓易维宁 . 遥感图像自适应去噪方法研究[J]. 大气与环境光学学报, 2011, 6(5): 368-376.

ZHANG Ji-yao, ZHANG Xie, LIU Xiao, YI Wei-ning. Investigation on Adaptive Denoising of Remote Sensing Image[J]. Journal of Atmospheric and Environmental Optics, 2011, 6(5): 368-376.

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