%0 Journal Article
%T 基于双重字典及联合特征的遥感图像超分辨率算法<br>Remote sensing image super resolution using dual dictionary pairs based on sparse presentation and multiple features
%A 杨晓敏
%A 吴
%A 炜
%A 严斌宇
%A 张莹莹？？
%J 四川大学学报 (自然科学版)
%D 2015
%X 为了解决低分辨率遥感图像超分辨重建的问题，本文提出了一种基于双重字典及联合特征的遥感图像超分辨率算法.超分辨率重建技术目的就是根据低分辨率图像重建出原始高分辨率图像的高频信息.本文将图像的高频信息分解成为主高频信息和残差高频信息两个部分，然后针对主高频信息和残差高频信息，分别训练主高频字典和残差高频字典，并结合稀疏表示方法对图像进行重构.同时，为了建立更能反映图像内部结构信息的字典，本文联合图像的不同的结构特征，建立统一的字典.本文算法对图像取得较好的复原效果，复原出的高分辨率图像更接近于真实图像，与其他方法相比具有更好的主观和客观质量.<br>In this paper, a super resolution method based on sparse dictionary and multiple futures is proposed for remote sensing images. Super resolution aims to reconstruct the high frequency detail from the low resolution image. In this paper, high frequency is decomposed into two parts: primary high frequency and residual high frequency. We proposed dual dictionary pairs, i.e. primitive sparse dictionary pair and residual sparse dictionary pair to recover primary high frequency and residual high frequency respectively. To describe the image more precise, the authors use multiple features to describe the structure of the image, and combine them together to present the image. Then use the combination futures to train the dictionary. The experimental results show that the proposed algorithm has a good performance, and the high resolution image generated by the proposed method is with better subjective and objective quality compared with other methods
%K 超分辨率
%K 稀疏表示 遥感图像
%K 字典学习&lt
%K br&gt
%K Super resolution Sparse representation Remote sensing image Dictionary learning
%U http://science.ijournals.cn/jsunature_cn/ch/reader/view_abstract.aspx?file_no=20155018&flag=1