改进的稀疏表示遥感图像超分辨重建

朱福珍; 刘越; 黄鑫; 白鸿一; 巫红

doi:10.3788/OPE.20192703.0718

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改进的稀疏表示遥感图像超分辨重建

信息科学 | 更新时间：2020-08-13

- 改进的稀疏表示遥感图像超分辨重建
- Remote sensing image super-resolution based on improved sparse representation
- 光学精密工程 2019年27卷第3期页码：718-725
- 作者机构：
  
  黑龙江大学电子工程学院，黑龙江哈尔滨 150080
- 作者简介：
  
  朱福珍(1978-)，女，黑龙江佳木斯人，博士后，副教授，硕士生导师，2011年于哈尔滨工业大学获得博士学位。主要从事图像超分辨、压缩感知、神经网络、深度学习等方向的研究。E-mail：zhufuzhen@hlju.edu.cn ZHU Fu-zhen, E-mail: zhufuzhen@hlju.edu.cn
  刘越(1995-)，女，黑龙江齐齐哈尔人，硕士研究生，主要从事图像处理、超分辨等方面的研究。E-mail: L18686923289@163.com
- 基金信息：
  
  国家自然科学基金资助项目(61601174);黑龙江省博士后科研启动金项目资助(LBH-Q17150);黑龙江省普通高等学校电子工程重点实验室(黑龙江大学)开放课题资助及省高校科技创新团队资助(2012TD007);黑龙江省省属高等学校基本科研业务费基础研究项目资助(KJCXZD201703);黑龙江省自然科学基金资助项目(F2018026)
- DOI：10.3788/OPE.20192703.0718
  中图分类号： TP394.1;TH691.9
- 收稿日期：2018-08-31，
  
  录用日期：2018-11-7，
  
  纸质出版日期：2019-03-15
- 稿件说明：
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朱福珍, 刘越, 黄鑫, 等. 改进的稀疏表示遥感图像超分辨重建[J]. 光学精密工程, 2019,27(3):718-725.

Fu-zhen ZHU, Yue LIU, Xin HUANG, et al. Remote sensing image super-resolution based on improved sparse representation[J]. Optics and precision engineering, 2019, 27(3): 718-725.
朱福珍, 刘越, 黄鑫, 等. 改进的稀疏表示遥感图像超分辨重建[J]. 光学精密工程, 2019,27(3):718-725. DOI： 10.3788/OPE.20192703.0718.

Fu-zhen ZHU, Yue LIU, Xin HUANG, et al. Remote sensing image super-resolution based on improved sparse representation[J]. Optics and precision engineering, 2019, 27(3): 718-725. DOI： 10.3788/OPE.20192703.0718.

摘要

为了进一步提高遥感图像超分辨效果，提高超分辨重建速度。针对以往稀疏超分辨算法中更容易丢失边缘信息和引入噪声的问题，本文改进了特征提取算子，以对称近邻滤波(SNN)代替高斯滤波，重点解决特征空间中的字典学习问题。首先，根据遥感图像退化模型生成训练样本图像，并分别对高、低分辨率遥感图像进行7×7分块，生成字典训练样本。然后，建立连接高、低分辨率图像空间的双参数联合稀疏字典，将字典学习过程中的稀疏系数分解为系数权值和字典原子的乘积，依据字典原子指标训练和更新字典，得到高低分辨率联合字典映射矩阵。最后，进行遥感图像超分辨稀疏重构。实验结果表明：与当前最先进的稀疏表示超分辨算法相比，本文算法得到的超分辨重建遥感图像的主观效果更好，恢复出更多的地物细节信息；客观评价参数峰值信噪比(PSNR)提高约1.7 dB，结构相似性(SSIM)提高约0.016。改进的稀疏表示超分辨算法可以有效地提高遥感图像超分辨效果，同时降低重建时间。

Abstract

To solve the problems of lost details and added noise in the previous sparse representation image super-resolution

an improved feature extraction algorithm was proposed to improve the image Super-Resolution Reconstruction (SRR) effect. The Gaussian filter was replaced by a symmetric nearest neighbor filter to speed up image super-resolution

and the problem of dictionary learning in the feature space was solved. First

sample training images were generated based on the remote sensing image degradation model

and high-low resolution images were divided into image patches sized 7×7. Then

a high-low resolution joint dictionary mapping matrix was generated after the dictionary was trained and updated. Finally

image super-resolution reconstruction was performed in sparse representation. Experimental results revealed that the proposed method reconstructed a higher-quality super-resolution image in less time. Simultaneously

as compared with the image obtained with the most advanced sparse representation super-resolution algorithm

the SRR resulting image contained more texture details of ground objects. In addition

the peak signal-to-noise ratio and structural similarity index measure were increased by approximately 1.7 dB and 0.016

respectively. Conclusion: The improved sparse representation SRR algorithm can effectively improve the SRR effect of remote sensing images and reduce the super-resolution reconstruction time.

关键词

Keywords

references

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