高光谱图像分块压缩感知采样及谱间预测重构

doi:10.3969/j.issn.0255-8297.2014.03.009

应用科学学报 ›› 2014, Vol. 32 ›› Issue (3): 281-286.doi: 10.3969/j.issn.0255-8297.2014.03.009

高光谱图像分块压缩感知采样及谱间预测重构

贾应彪，冯燕，袁晓玲，魏江

西北工业大学电子信息学院，西安710129

收稿日期:2012-04-06 修回日期:2012-11-28 出版日期:2014-05-31 发布日期:2012-11-28
作者简介:冯燕，教授，博导, 研究方向：高光谱图像压缩和分类、图像处理和智能信息处理，E-mail: sycfy@nwpu.edu.cn
基金资助:
国家自然科学基金(No.61071171)资助

Block Compressed Sensing Sampling and Reconstruction Using Spectral Prediction for Hyperspectral Images

JIA Ying-biao, FENG Yan, YUAN Xiao-ling, WEI Jiang

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China

Received:2012-04-06 Revised:2012-11-28 Online:2014-05-31 Published:2012-11-28

摘要/Abstract

摘要： 压缩感知理论提供了一种新的数据采集思路. 基于该理论提出了一种高光谱数据采集和图像重构方法，以波段分组的方式将高光谱各波段分为参考波段和普通波段，对各波段图像单独采用分块压缩感知测量以获取高光谱数据. 在图像重构过程中，参考波段采用平滑投影Landweber算法重构. 对于普通波段，结合谱间预测和平滑投影Landweber提出了一种新算法: 先采用谱间双向预测得到预测图像，然后对预测图像进行分块压缩感知测量获得测量值，并计算它与该波段原测量值之间的差值，再由测量差值重构预测差值来迭代恢复原波段图像. 该方法在数据重构过程中充分考虑了高光谱图像的谱间相关性和空间相关性，能提高图像重构精度. 实验结果表明，利用所提出的方法重构高光谱图像，其性能优于多向量压缩感知方法和分块压缩感知测量后直接对各波段图像单独重构的方法.

关键词: 高光谱图像, 压缩感知, 分块测量, 谱间双向预测, 重构

Abstract: Compressed sensing (CS) provides a new method for data acquisition. Ahyperspectral images CS
methodology is proposed in this paper. In the proposed framework, hyperspectral images are divided into
several groups, and each group consists of a reference band followed by some common bands. Random measurements of the individual spectral bands are obtained using block CS independently. In image reconstruction,the reference bands are reconstructed with the smoothed projected Landweber algorithm, and the common bands with a new reconstruction algorithm. The algorithm is implemented as follows: 1) Obtain predicted values of the common bands using the spectral bidirectional prediction. 2) Calculate measurement differences using block observation on the predicted values. 3) Reconstruct the images and their corresponding prediction differences in an iterative fashion. This method can improve reconstruction quality as it has fully considered the spectral and spatial correlations. Experimental results reveal that reconstruction performance of the proposed method is substantially superior to that by applying 2-D image reconstruction independently and that of a multiple-vector CS variant method.

Key words: hyperspectral image, compressed sensing (CS), block observation, spectral bidirectional prediction, reconstruction

中图分类号:

TP751.2

贾应彪，冯燕，袁晓玲，魏江. 高光谱图像分块压缩感知采样及谱间预测重构[J]. 应用科学学报, 2014, 32(3): 281-286.

JIA Ying-biao, FENG Yan, YUAN Xiao-ling, WEI Jiang. Block Compressed Sensing Sampling and Reconstruction Using Spectral Prediction for Hyperspectral Images[J]. Journal of Applied Sciences, 2014, 32(3): 281-286.

参考文献

[1] DONOHO D L. Compressed sensing[J]. IEEE Transaction on Information Theory, 2006, 52 (4): 1289-1306.

[2] CANDÈS E, ROMBERG J, TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information [J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.

[3] BARANIUK R G. Compressive sensing[J]. IEEE on Signal Processing Magazine, 2007, 24(4): 118-121.

[4] WAGADARIKAR A, JOHN R, WILLETT R, et al. Single disperser design for coded aperture snapshot spectral imaging [J]. Applied Optics, 2008, 47: B44-B51.

[5] KITTLE D, CHOI K, WAGADARIKAR A, et al. Multi-frame image estimation for coded aperture snapshot spectral imagers [J]. Applied Optics, 2010, 49: 6824-6833.

[6] SHU Xian Biao, AHUJA Narendra. Imaging via three-dimensional compressive sampling (3DCS) [C]//13th International Con-ference on Computer Vision (ICCV), 2011:439-446.

[7] DUARTE M F, Richard G. BARANIUK R G. Kronecker compressive sensing[J]. IEEE Transactions on Image Processing, 2012,21(2): 494-504.

[8] 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]. 西安电子科技大学学报，2011, 38(3): 37-41.

LIU Haiying, LI Yunsong, WU Chengke , Lv Pei. Compressed hyperspectral image sensing based on inter-band prediction [J]. Journal OF Xidian University, 2011, 38(3): 37- 41. (in Chinese).

[9] JI S, DUNSON D, CARIN L. Multitask compressive sensing[J]. IEEE Transaction on Signal Process, 2009, 57(1): 92-106.

[10] GAN L. Block compressed sensing of natural images [C]//15th International Conference on Digital Signal Processing, 2007:403-406.

[11] HAUPT J, NOWAK R. Signal reconstruction from noisy random projections[J]. IEEE Transactions on Information Theory, 2006, 52(9): 4036-4048.

[12] MUN S, FOWLER J E. Block compressed sensing of images using directional transforms[C]//IEEE International Conference on Image Processing, 2009: 3021-3024.

[13] CHRISTOPHE E, LÉGER D, MAILHES C. Quality criteria benchmark for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(9): 2103-2114.

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高光谱图像分块压缩感知采样及谱间预测重构

Block Compressed Sensing Sampling and Reconstruction Using Spectral Prediction for Hyperspectral Images

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