期刊文献+

一种多狭缝推扫式光谱成像方法

Push-broom spectral imaging method based upon multi-slit
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摘要 高质量、高分辨率的遥感光谱数据获取受限于成像系统的光通量.针对传统推扫式的光谱仪当增加狭缝的光通量以提高信噪比时会降低空间分辨率这个问题,从信号处理角度为推扫式光谱成像系统建模,引入压缩感知理论,使得系统可以通过增加狭缝数目的方式来增加光通量.在理论上,该方法增加了光通量而没有降低空间分辨率.在仿真实验中,光通量增大128倍,用25%的曝光频率就可以很好地获得512×512的光谱图像.该光谱成像方法在遥感技术领域以较少的曝光次数、存储传输较少的数据就可以获得高质量的光谱数据. The acquisition of high image quality and high resolution spectral data is limited by light flux.A push-broom spectral imaging should reduce the spatial resolution if it amplified its light flux to increase its signal noise ratio(SNR).According to this problem,the theory of compressive sensing(CS)is introduced for modeling the push-broom spectral imaging system from the signal processing analysis,so that the number of slits of the imaging system can be increased to amplify its light flux.Under the guidance of the theory of compressive sensing,the light flux can increase without reducing the spatial resolution.In the simulation,if its exposure frequency dropped to 1/4the original,and its light flux increased to 128 times the original,the spectral image with the resolution of 512×512could be well obtained.This method is suitable for remote sensing by using a smaller number of times for imaging and less memory for storage and transmission compared with the traditional one.
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2016年第2期29-34,共6页 Journal of Xidian University
基金 国家自然科学基金资助项目(31300473 61372131 61227004 61003148) 中央高校基本科研业务费专项资金资助项目(K5051202050 K5051399020) 福建省自然科学基金资助项目(2014J01073)
关键词 光谱 成像 压缩感知 空间分辨率 推扫式 spectrum imaging compressed sensing special resolution push-broom
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  • 1Cand6s E, 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.
  • 2Donoho D L. Compressed Sensing [J]. IEEE Trans on Information Theory, 2006, 52(4): 1289-1306.
  • 3Duarte M, Davenport M, Takhar D, et al. Single-Pixel Imaging via Compressive Sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 83-91.
  • 4Wakin M, Laska J, Duarte M, et al. Compressive Imaging for Video Representation and Coding [C/OL]. [2011-12- 30] . http://inside, mines, edu/- mwakin/paper/pcs-camera.*pdf.
  • 5Shi G M, Gao D H, Song X X, et al. High-Resolution lmaging Via Moving Random Exposure and Its Simulation [J]. IEEE Trans on Image Processing, 2011, 20(1) : 276-282.
  • 6Taubeck G, Hlawatsch F. A Compressed Sensing Technique for OFDM Channel Estimation in Mobile Environments: Exploiting Channel Sparsity for Reducing Pilots [C]//IEEE International Conference on Acoustics, Speech, and Signal Processing. l.as Vegas: IEEE, 2008: 2885-2888.
  • 7Wang I. J, Wu X I., Shi G M. A Compressive Sensing Approach of Multiple Descriptions for Network Multimedia Communication [C]//IEEE 10th Workshop on Multimedia Signal Processing. Cairns: IEEE, 2008: 445-449.
  • 8Wright J, Yang A, Ganesh A. Robust Face Recognition Via Sparse Representation, and Its Online Supplementary Material [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(2) : 210-227.
  • 9l.iu B, Fu P, Meng S W. Compressive Sensing Signal Detection Algorithm Based on Location Information of Sparse Coefficients [J]. International Journal of Digital Content Technology and Its Applications, 2010, 4(8): 79-85.
  • 10Tropp J, Wakin M, Duarte M. Random Filters for Compressive Sampling and Reconstruction [C]//IEEE International Conference on Acoustics, Speech, and Signal Processing. Toulouse: IEEE, 2006: 872-875.

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