-
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。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.
-
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
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
表 1 m=5时重构结果
Table 1. Reconstruction results when m=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 2 b=3时重构结果
Table 2. Reconstruction results when b=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
-
[1] 童庆禧, 张兵, 张立福.中国高光谱遥感的前沿进展[J].遥感学报, 2016, 20(5):689-707. http://www.opticsjournal.net/abstract.htm?id=OJ140102000067SpVrYuTONG Q X, ZHANG B, ZHANG L F. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5):689-707.(in Chinese) http://www.opticsjournal.net/abstract.htm?id=OJ140102000067SpVrYu [2] 王忠良, 冯燕, 肖华, 等.高光谱图像的分布式压缩感知成像与重构[J].光学 精密工程, 2015, 23(4):1131-1137. http://www.cnki.com.cn/Article/CJFDTotal-GXJM201504028.htmWANG ZH L, FENG Y, XIAO H, et al.. Distributed compressive sensing imaging and reconstruction of hyperspectral imagery[J]. Opt. Precision Eng., 2015, 23(4):1131-1137.(in Chinese) http://www.cnki.com.cn/Article/CJFDTotal-GXJM201504028.htm [3] 汤毅, 万建伟, 粘永健.高光谱图像的分布式近无损压缩[J].光学学报, 2015, 35(3):0928001. http://www.cnki.com.cn/Article/CJFDTotal-GXXB201503010.htmTANG Y, WAN J W, NIAN Y J. Distributed nearlossless compression of hyperspectral images[J]. Acta Optica Sinica, 2015, 35(3):0928001.(in Chinese) http://www.cnki.com.cn/Article/CJFDTotal-GXXB201503010.htm [4] 刘长虹, 夏青.高光谱成像在肉品品质及安全检测方面的应用[J].合肥工业大学学报(自然科学版), 2016, 39(4):554-559. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hfgydxxb201604024LIU CH H, XIA Q. Application of hyperspectral imaging in evaluating quality and safety of meat[J]. Journal of Hefei University of Technology, 2016, 39(4):554-559.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hfgydxxb201604024 [5] 梅风华, 李超, 张玉鑫.光谱成像技术在海域目标探测中的应用[J].中国光学, 2017, 10(6):708-718. http://www.chineseoptics.net.cn/CN/abstract/abstract9565.shtmlMEI F H, LI CH, ZHANG Y X. Application of spectral imaging technology in maritime target detection[J]. Chinese Optics, 2017, 10(6):708-718.(in Chinese) http://www.chineseoptics.net.cn/CN/abstract/abstract9565.shtml [6] 张达, 郑玉权.高光谱遥感的发展与应用[J].光学与光电技术, 2013, 11(3):67-73. https://www.wenkuxiazai.com/doc/5f6a79dbf524ccbff12184ea.htmlZHANG D, ZHENG Y Q.Hyperspectral remote sensing and its development and application review[J]. Optics & Optoelectronic Technology, 2013, 11(3):67-73.(in Chinese) https://www.wenkuxiazai.com/doc/5f6a79dbf524ccbff12184ea.html [7] 王跃明, 韦丽清, 郎均慰, 等.先进焦平面与光谱成像技术现状[J].光学与光电技术, 2014, 12(1):7-13. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hwyhmb201501010WANG Y M, WEI L Q, LANG J W, et al.. Status of advanced focal plane arrays and spectral imaging technology[J]. Optics & Optoelectronic Technology, 2014, 12(1):7-13.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hwyhmb201501010 [8] 马芳苑, 王跃明.基于CMOS的轻小型高光谱仪的发展及应用[J].光学与光电技术, 2016, 14(4):84-89. https://www.researchgate.net/profile/Yong_Yao2/publication/271831506_Characteristics_of_typical_optical_systems_with_diffractive_gratings_of_micro-spectrometers_and_improvement_of_spectrometer's_resolution/links/54ffc99a0cf2eaf210bc334d.pdf?inViewer=true&pdfJsDownload=true&disableCoverPage=true&origin=publication_detailMA F Y, WANG Y M. Development and application of the light and small CMOS hyperspectral camera[J]. Optics & Optoelectronic Technology, 2016, 14(4):84-89.(in Chinese) https://www.researchgate.net/profile/Yong_Yao2/publication/271831506_Characteristics_of_typical_optical_systems_with_diffractive_gratings_of_micro-spectrometers_and_improvement_of_spectrometer's_resolution/links/54ffc99a0cf2eaf210bc334d.pdf?inViewer=true&pdfJsDownload=true&disableCoverPage=true&origin=publication_detail [9] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. doi: 10.1109/TIT.2006.871582 [10] CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transaction on Information Theory, 2006, 52(2):489-508. doi: 10.1109/TIT.2005.862083 [11] BIOUCAS-DIA J M, FIGUEIREDO M A T. A newTwIST:two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12):2992-3004. doi: 10.1109/TIP.2007.909319 [12] WRIGHT S J, NOWAK R D, FIGUEIREDO M A T. Sparse reconstruction by separable approximation[J]. IEEE Transactions on Signal Processing, 2009, 57(7):2479-2493. doi: 10.1109/TSP.2009.2016892 [13] 蒋沅, 苗生伟, 罗华柱, 等.Lp范数压缩感知图像重建优化算法[J].中国图象图形学报, 2017, 22(4):0435-0442. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201704003.htmJIANG Y, MIAO SH W, LUO H ZH, et al.. Improved search algorithm for compressive sensing image recovery based on Lp norm[J]. Journal of Image and Graphics, 2017, 22(4):0435-0442.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201704003.htm [14] WANG A, LIU L, ZENG B, et al.. Progressive image coding based on an adaptive block compressed sensing[J]. IEICE Electronics Express, 2011, 8(8):575-581. doi: 10.1587/elex.8.575 [15] CHEN H W, KANG L W, LU CH S. Dynamic measurement rate allocation for distributed compressive video sensing[J]. Proc. of SPIE, 2010, 7744:1117-1126. https://core.ac.uk/display/52370203 [16] 左觅文, 常侃, 施静兰, 等.基于帧间相关性的自适应采样率分块视频压缩感知[J].电讯技术, 2015, 55(4):360-365. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianxjs201504002ZUO M W, CHANG K, SHI J L, et al.. Adaptive rate block compressive video sensing based on inter-frame correlation[J]. Telecommunication Engineering, 2015, 55(4):360-365.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianxjs201504002 [17] HOU Y, ZHANG Y N. Interband prediction compressed sensing reconstruction algorithm for hyperspectral image[C]. 20164th International Workshop on Earth Observation and Remote Sensing Applications(EORSA), IEEE, Guangzhou, China, 2016: 465-469. [18] 王相海, 宋海鹰, 宋传鸣, 等.谱间和帧内协同稀疏的高光谱图像压缩感知模型[J].中国科学:信息科学, 2016, 46(3):361-375. http://scis.scichina.com/cn/2016/N112014-00402.pdfWANG X H, SONG H Y, SONG CH M, et al.. Hyperspectral image compressed sensing model based on the collaborative sparsity of the intra-frame and inter-band[J]. Scientia Sinica Informations, 2016, 46(3):361-375.(in Chinese) http://scis.scichina.com/cn/2016/N112014-00402.pdf [19] HOU Y, LIU J. Hyperspectral image compressed sensing based on effective spectral reconstruction[C]. 2016 International Symposium on Computer, Consumer and Control(IS3C), IEEE, Xi'an, China, 2016: 660-663. -
下载:
图(7) / 表(3)
计量
- 文章访问数: 2789
- HTML全文浏览量: 983
- PDF下载量: 297
- 被引次数: 0
