Infrared Remote Sensing Video Staring Imagery Based on Compressed Sensing Online Sparse

LI Sheng-liang; LIU Kun; ZHANG Feng; XIAO Long-long; HAN Da-peng

doi:10.3969/j.issn.0372-2112.2015.03.016

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Infrared Remote Sensing Video Staring Imagery Based on Compressed Sensing Online Sparse

更新时间：2025-07-16

- Infrared Remote Sensing Video Staring Imagery Based on Compressed Sensing Online Sparse
- Acta Electronica Sinica Vol. 43, Issue 3, Pages: 518-522(2015)
- 作者机构：
  
  1. 国防科技大学航天科学与工程学院,湖南,长沙,410073
  2. 装备学院航天指挥系,北京,101416
  3. 国防科技大学航天科学与工程学院,湖南,长沙,410073
  4. 装备学院航天指挥系,北京,101416
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61271440)
- DOI：10.3969/j.issn.0372-2112.2015.03.016
  CLC： TN911.73
- Published：2015
- 稿件说明：
移动端阅览
LI Sheng-liang, LIU Kun, ZHANG Feng, et al. Infrared Remote Sensing Video Staring Imagery Based on Compressed Sensing Online Sparse[J]. Acta Electronica Sinica, 2015, 43(3): 518-522.
DOI：

LI Sheng-liang, LIU Kun, ZHANG Feng, et al. Infrared Remote Sensing Video Staring Imagery Based on Compressed Sensing Online Sparse[J]. Acta Electronica Sinica, 2015, 43(3): 518-522. DOI： 10.3969/j.issn.0372-2112.2015.03.016.

摘要

压缩感知理论是一种新兴的数字信号处理方法

在红外遥感视频成像中有很好的应用前景.为了提高红外视频遥感成像的效果

提出了基于压缩感知在线稀疏的红外遥感视频凝视成像方法.介绍了该方法的视频图像分块压缩采样策略

视频图像在线稀疏方法

以及视频图像重构方法.重点研究了有运动目标的红外遥感图像在线稀疏表示方法

提出了迭进分块训练样本的设计方法和累积冗余字典构造方法.通过数值计算可以看出

该方法提高了遥感红外图像的重构效果.

Abstract

Compressed sensing (CS)

which captures and represents compressible signals at a sampling rate significantly below the Nyquist rate

serves as a new framework for signal sampling and reconstruction based on signal sparsity or compressibility.CS has a good application prospect in remote sensing imagery.In order to improve the efficiency of infrared remote sensing video

the infrared remote sensing video staring imagery based on compressed sensing online sparse is proposed.We introduce the block compression sampling strategy for video

and present the online sparse method and video reconstruction method.The online sparse approach for the motion objects in video is focused on.The iterative block training samples and the accumulation redundant dictionary is designed.Experimental results are presented to show that the infrared remote sensing video reconstruction effect is improved by this method.

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