一种高重构质量低复杂度的高光谱图像压缩感知

doi:10.3969/j.issn.1001-2400.2011.03.007

J4 ›› 2011, Vol. 38 ›› Issue (3): 37-41+120.doi: 10.3969/j.issn.1001-2400.2011.03.007

一种高重构质量低复杂度的高光谱图像压缩感知

刘海英¹；李云松¹；吴成柯¹；吕沛²

(1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071；
2. 中国科学院西安光学精密机械研究所，陕西西安 710119)

收稿日期:2010-04-27 出版日期:2011-06-20 发布日期:2011-07-14
通讯作者: 刘海英
作者简介:刘海英(1982-)，女，西安电子科技大学博士研究生，E-mail: hyliu@mail.xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(60802076,60702058)|高等学校创新引智计划资助项目(B08038)|中央高校基本科研业务费专项资金资助项目(JY10000901007)

Compressed hyperspectral image sensing based on interband prediction

LIU Haiying¹；LI Yunsong¹；WU Chengke¹；Lü Pei²

(1. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an 710071， China|
2. Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119， China)

Received:2010-04-27 Online:2011-06-20 Published:2011-07-14
Contact: LIU Haiying

摘要/Abstract

摘要：

针对高光谱图像谱间相关性较强的特点，提出了一种基于谱间预测的压缩感知算法．编码端通过线性预测算法，估计出相邻波段的预测系数作为辅助信息传送到解码端，再对图像进行独立地随机观测和量化．解码端根据预测系数和已重构相邻波段，估计出当前波段的预测波段，用来修正重构算法的初始值和收敛准则．最后用改进的重构算法解码当前波段．实验结果表明，在相同观测值数目下，该算法的PSNR提高了大约1.2dB，解码复杂度明显降低，而且编码复杂度低、易于硬件实现．

关键词: 高光谱图像, 压缩感知, 线性预测, 优化问题

Abstract:

A new compression algorithm for hyperspectral images based on compressed sensing is proposed which has the advantages of high reconstruction quality and low complexity by exploiting the strong spectral correlations. At the encoder, the prediction parameter between the neighboring bands is first estimated using the prediction algorithm and transmitted to the decoder. The random measurements of each band are then made, quantized and transmitted to the decoder independently. At the decoder, a new reconstruction algorithm with the proposed initialization and stopping criterion is applied to reconstruct the current band with the assistance of its prediction band, which is derived from the previous reconstructed neighboring band and the received prediction parameter using the prediction algorithm. Experimental results show that the proposed algorithm not only obtains a gain of about 1.2dB but also greatly decreases decoding complexity. In addition, our algorithm has the characteristics of low-complexity encoding and easy hardware implementation.

Key words: hyperspectral imagery, compressed sensing, linear prediction, optimization

刘海英；李云松；吴成柯；吕沛. 一种高重构质量低复杂度的高光谱图像压缩感知[J]. J4, 2011, 38(3): 37-41+120.

LIU Haiying；LI Yunsong；WU Chengke；Lü Pei. Compressed hyperspectral image sensing based on interband prediction[J]. J4, 2011, 38(3): 37-41+120.

参考文献

［1］ Miekainen J, Toivanen P. Clustered DPCM for the Lossless Compression of Hyperspectral Images［J］. IEEE Trans on Geosci Remote Sensing, 2003, 41(12): 2943-2946.
［2］ Zhang J, Liu G Z. A Novel Lossless Compression for Hyperspectral Images by Context-Based Adaptive Classified Arithmetic Coding in Wavelet Domain［J］. IEEE Geosci Remote Sensing Letters, 2007, 4(3): 461-465.
［3］ Ryan M J, Arnold J F. The Lossless Compression of AVIRIS Images by Vector Quantization［J］. IEEE Trans on Geosci Remote Sensing, 1997, 35(3): 546-550.
［4］ Candes E J. Compressive Sampling［C/OL］. ［2009-06-20］. http://www-stat.stanford.edy/candes/papers/CompressiveSampling.pdf.
［5］ Candes E J, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information ［J］. IEEE Trans on Information Theory, 2006, 52(2): 489-509.
［6］ Donoho D L. Compressed Sensing［J］. IEEE Trans on Information Theory, 2006, 52(4): 1289-1306.
［7］ Romberg J. Imaging via Compressive Sampling［J］. IEEE Signal Processing Magazine, 2008, 25(2): 14-20.
［8］ Candes E J, Tao T. Near Optical Signal Recovery from Random Projections: Universal Encoding［J］. IEEE Trans on Information Theory, 2006, 52(12):5406-5425.
［9］刘丹华, 石光明, 周佳社, 等. 基于Compressed Sensing框架的图像多描述编码方法［J］. 红外与毫米波学报, 2009, 28(4): 298-302.
Liu Danhua, Shi Guangming, Zhou Jiashe, et al. New Method of Multiple Description Coding for Image Based on Compressed Sensing［J］. J Infrared Millim Waves, 2009, 28(4): 298-302.
［10］ Gan L, Do T T, Tran T D. Fast Compressive Imaging Using Scrambled Block Hadamard Ensemble［C/OL］. ［2009-08-16］. http://dsp.rice.edu/sites/dsp.rice.edu/files/cs/srambled_blk_WHT.pdf.
［11］ Figueiredo M A T, Nowak R D, Wright S J. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems ［J］. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
［12］ Brites C, Ascenso J, Pereira F. Studying Temporal Correlation Noise Modeling for Pixel Based Wyner-ziv Video Coding［C］//IEEE International Conference of Image Processing. Atlanta: IEEE, 2006: 273-276.

[1]	张锦涛,雷杰,吴凌云,黄碧莹,李云松. 一种改进的高光谱端元提取算法及其FPGA实现[J]. 西安电子科技大学学报, 2019, 46(4): 22-27.
[2]	周雪芹,冯象初,景明利. 一种自适应的拟牛顿投影稀疏信号恢复算法[J]. 西安电子科技大学学报, 2019, 46(3): 14-19.
[3]	曾创展,朱卫纲,贾鑫. 一种稀疏孔径逆合成孔径雷达成像算法[J]. 西安电子科技大学学报, 2019, 46(3): 123-129.
[4]	杨建功,汪西莉,刘侍刚. 融合谱-空域信息的DBM高光谱图像分类方法[J]. 西安电子科技大学学报, 2019, 46(3): 109-115.
[5]	孙宸, 成立业. 空间感知矩阵学习的极化SAR图像分类[J]. 西安电子科技大学学报, 2018, 45(6): 92-98.
[6]	吕治国;李颖. 采用压缩感知的贝叶斯信道估计算法[J]. 西安电子科技大学学报, 2018, 45(2): 13-18+25.
[7]	李玮;邓维波;杨强;索莹;MIGLIORE Marco Donald. 采用压缩感知的阵列失效单元诊断方法[J]. 西安电子科技大学学报, 2018, 45(2): 160-165.
[8]	刘学文;肖嵩;薛晓. 经验模态分解构造观测矩阵的方法[J]. 西安电子科技大学学报, 2018, 45(1): 35-41.
[9]	李燕龙;陈晓;詹德满;王俊义. 非正交多址接入中稀疏多用户检测方法[J]. 西安电子科技大学学报, 2017, 44(3): 151-156.
[10]	权磊;肖嵩;薛晓;李颖. 低复杂度压缩感知中的快速观测方法[J]. 西安电子科技大学学报, 2017, 44(1): 106-111.
[11]	吴振华;刘宏伟;张磊;寇娜. 超材料孔径成像雷达的辐射场自由度评价[J]. 西安电子科技大学学报, 2016, 43(6): 39-44.
[12]	王勇;乔倩倩;杨笑宇;徐文娟;贾拯;陈楚楚;高全学. 结合自适应字典学习的稀疏贝叶斯重构[J]. 西安电子科技大学学报, 2016, 43(4): 1-4+122.
[13]	王珏;王俊;武勇;邓亚琦;罗振. 缺失孔径下多基地外辐射源雷达成像算法[J]. 西安电子科技大学学报, 2016, 43(3): 25-30.
[14]	王峰;向新;易克初;熊磊. 支撑驱动的非凸压缩感知恢复算法[J]. 西安电子科技大学学报, 2016, 43(2): 1-5+28.
[15]	林耀海;石光明;王立志;高大化. 一种多狭缝推扫式光谱成像方法[J]. 西安电子科技大学学报, 2016, 43(2): 29-34.

一种高重构质量低复杂度的高光谱图像压缩感知

Compressed hyperspectral image sensing based on interband prediction

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10