基于稀疏表示和自适应字典学习的“高分一号”遥感图像去噪  

The Remote Sensing Image Denoising of “The First Satellite of High Resolution” Based on Sparse Representation and Dictionary Learning

作　　者：秦振涛[1,2] 杨武年[1] 潘佩芬[1] 

机构地区：[1]成都理工大学地学空间信息技术国土资源部重点实验室/遥感与GIS研究所,成都610059 [2]攀枝花学院数学与计算机学院,四川攀枝花617000

出　　处：《光电工程》2013年第9期16-21,共6页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(41071265;41372340);高等学校博士学科点专项科研基金(20105122110006)

摘　　要：对高分辨率遥感图像进行去噪是遥感研究中的一个重要难题。本文提出了一种新的基于稀疏表示的高分辨率遥感图像去噪算法,该算法根据加噪高分辨率遥感图像的特点利用K-SVD算法自适应的学习得到能高效描述遥感图像内容的字典,利用稀疏表示实现去噪,并且保留原图像的有用信息。通过对"高分一号"获取的遥感图像进行实验表明,该算法能较好地滤除遥感图像的噪声,提高了图像的峰值信噪比,该方法比其他字典学习算法及其他去噪算法具有更好的性能。Denoising the high resolution remote sensing images is a difficult problem in the relative research field of remote sensing. A novel algorithm for denoising the high resolution remote sensing images is proposed based on sparse representation. A dictionary which has an efficient description of remote sensing image content is obtained based on K-SVD algorithm according to the characteristics of the added noise of high spatial resolution remote sensing images Denoising is realized by using sparse representation, and the useful information of the image is kept. The experimental results of the remote sensing images obtained by ＂the first satellite of high resolution＂ show that the algorithm can filter out the noise in the image more effectively and improve the PSNR, and this method has better performance than other dictionary learning algorithms and other denoising algorithms

关 键 词：稀疏表示 图像去噪 字典学习 K-SVD 高分一号 

分 类 号：TN911.73[电子电信—通信与信息系统] TN391[电子电信—信息与通信工程]