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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

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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?#21462;?#38024;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?#24179;?#37051;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?#36153;?#26412;图像, 并分别对高、低分辨?#23460;?#24863;图像进行7×7分块, 生成字典训?#36153;?#26412;。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?#29486;?#20856;?#25104;?#30697;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, ?#25351;?#20986;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?#26434;行?#22320;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.

Newport宣传-MKS新实验室计划
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中图分类号:TP394.1;TH691.9

DOI:10.3788/ope.20192703.0718

所属?#25913;浚?a href='../Journals/JColumnList?cid=892' title='查看该期刊此?#25913;?#19979;其他论文' class='TagKey' target='_blank'>信息科学

基金项目:国家自然科学基金资助项目(No.61601174);黑龙江省博士后科研启动金项目资助(No.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(No.2012TD007);黑龙江省省属高等学校基?#31350;?#30740;业务费基础研究项目资助(No. KJCXZD201703);黑龙江省自然科学基金资助项目(No.F2018026)

收稿日期:2018-08-31

修改稿日期:2018-11-07

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作者单位    点击查看

朱福珍:黑龙江大学 电子工程学院, 黑龙江 哈尔滨 150080
刘 越:黑龙江大学 电子工程学院, 黑龙江 哈尔滨 150080
黄 鑫:黑龙江大学 电子工程学院, 黑龙江 哈尔滨 150080
白鸿一:黑龙江大学 电子工程学院, 黑龙江 哈尔滨 150080
巫 红:黑龙江大学 电子工程学院, 黑龙江 哈尔滨 150080

联系人作者:朱福珍([email protected])

备注: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。

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引用该论文

ZHU Fu-zhen,LIU Yue,HUANG Xin,BAI Hong-yi,WU Hong. 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,白鸿一,巫 红. 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

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