动态测量的高光谱图像压缩感知 -

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动态测量的高光谱图像压缩感知

doi:10.3788/CO.20181104.0550

北京理工大学光电学院光电成像技术与系统教育部重点实验室, 北京 100081

基金项目:

国家自然科学基金重大科学仪器专项61527802

详细信息

作者简介:
闫歌(1993-), 女, 河南洛阳人, 硕士研究生, 2015年于西安电子科技大学取得学士学位, 主要从事高光谱图像压缩感知方面的研究。E-mail:yange1206@bit.edu.cn
许廷发(1968—), 黑龙江肇东人, 男, 博士(后), 教授, 博士生导师, 1992年毕业于东北师范大学物理系, 2000年于东北师范大学物理系获得硕士学位, 2004年于中国科学院长春光学精密机械与物理研究所获得博士学位, 2006年华南理工大学电子与信息学院完成博士后工作。主要从事红外/可见目标跟踪与识别技术、光电信号检测技术、高光谱遥感影像处理技术等方面的研究。E-mail:ciom_xtf1@bit.edu.cn
马旭(1983—), 北京人, 男, 博士, 教授, 博士生导师, 2005年于清华大学电子工程系获得学士学位; 2009年于美国特拉华大学电子与计算机工程系获得博士学位; 2009~2010年于美国加州大学伯克利分校担任博士后研究工作。主要从事计算成像、图像处理与信号处理方面的研究。E-mail:maxu@bit.edu.cn

中图分类号:TP751
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出版历程
- 收稿日期:2017-11-01
- 修回日期:2018-01-05
- 刊出日期:2018-08-01

Hyperspectral image compression sensing based on dynamic measurement

School of Optoelectronics, MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China

Funds:

Major Science Instrument Program of the National Natural Science Foundation of China61527802

More Information

Corresponding author:XU Ting-fa, E-mail:ciom_xtf1@bit.edu.cn;MA Xu, E-mail:maxu@bit.edu.cn

摘要

摘要:针对高光谱图像相邻波段之间具有强光谱相关性的特点，为了提高高光谱图像压缩感知的重构效果，本文提出一种利用边缘信息设计动态测量率的压缩感知算法。首先，通过随机投影的分块压缩感知方法对每个图像块以固定测量率采样，重构出单波段图像作为其他波段的先验信息，并对其提取出图像边缘区域；然后，根据每个图像块边缘信息的丰富程度来自适应分配测量值。在固定总测量数的前提下，对不同图像块分配不同的测量次数。最后，利用分配好的测量次数对其余波段进行采集和重构。仿真结果表明，在相同总测量数情况下，本文提出的动态测量算法重构出的高光谱图像质量(PSNR)与传统固定测量压缩感知策略相比提高了1~4 dB，相比较下的重构时间也减少，在成功重构高光谱图像的基础上更增强了细节处的图像质量。
- 高光谱图像/
- 压缩感知/
- 动态测量率/
- 谱间相关性
Abstract:Based on the strong spectral correlation between adjacent bands of hyperspectral images, we proposes a compressed sensing algorithm that uses edge information to design dynamic measurement rate to improve the reconstruction effect of compressive sensing in hyperspectral images. First, each image block is sampled at a fixed measurement rate by a random projection block-compressive sensing method, a single-band image is reconstructed as a priori information of other bands, and an image edge region is extracted therefrom; then, the measurement values are adaptively assigned according to the richness of the edge information of each image block. With a certain total number of measurements, different number of measurements is assigned to different image blocks. Finally, the remaining wave bands are collected and reconstructed with the assigned measurements. The simulation results show that under the same total number of measurements, the hyperspectral image quality(Peak Signal to Noise Ratio(PSNR)) reconstructed by the dynamic measurement algorithm proposed in this paper is 1-4 dB higher than the traditional fixed-measurement compressive sensing strategy. Moreover, the reconstruction time is also reduced, and the image quality at the detail is further enhanced based on the successful reconstruction of the hyperspectral images.
- hyperspectral image/
- compressive sensing/
- dynamic measurement rate/
- inter-spectral correlation

HTML全文

图 1利用边缘信息的动态测量压缩感知高光谱图像重构算法

Figure 1.CS hyperspectral image reconstruction algorithm based on edge information

下载: 全尺寸图片幻灯片

图 2非参考波段动态测量率的自适应采样流程图

Figure 2.Adaptive sampling flow chart of dynamic measurement rate in non-reference band

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图 3参考波段重构图像

Figure 3.Reconstructed image of key band

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图 4参考波段的边缘区域划分

Figure 4.Edge region extraction of key band

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图 5非参考波段重构结果比较

Figure 5.Reconstruction results comparison for non-key bands

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图 6测量值数目分配情况

Figure 6.Allocation of the number of measurements

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图 7第10波段重构结果比较

Figure 7.Reconstruction results comparison for band 10

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表 1m=5时重构结果

Table 1.Reconstruction results whenm=5

宽度b	PSNR/dB	Time/s
1	29.920 3	13.572 1
2	29.963 3	13.842 0
3	29.990 3	14.030 5
4	29.905 7	14.469 5
5	28.751 8	15.053 6

下载: 导出CSV

表 2b=3时重构结果

Table 2.Reconstruction results whenb=3

固定测量值m	PSNR/dB	Time/s
3	30.374 2	12.987 4
4	30.221 6	13.616 3
5	30.048 7	14.058 7

下载: 导出CSV

表 3非参考波段重构PSNR(dB)对比

Table 3.Comparison of reconstructed image PSNR in non-key bands

采样率R	本文方案	固定测量方案
0.1	23.133 5	22.067 5
0.2	27.504 6	24.388 4
0.3	29.790 7	26.498 9
0.4	31.915 6	28.631 2
0.5	33.209 7	30.326 5

下载: 导出CSV

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