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压缩感知理论在图像处理领域的应用

朱明 高文 郭立强

朱明, 高文, 郭立强. 压缩感知理论在图像处理领域的应用[J]. 中国光学(中英文), 2011, 4(5): 441-447.
引用本文: 朱明, 高文, 郭立强. 压缩感知理论在图像处理领域的应用[J]. 中国光学(中英文), 2011, 4(5): 441-447.
ZHU Ming, GAO Wen, GUO Li-qiang. Application of compressed sensing theory in image processing[J]. Chinese Optics, 2011, 4(5): 441-447.
Citation: ZHU Ming, GAO Wen, GUO Li-qiang. Application of compressed sensing theory in image processing[J]. Chinese Optics, 2011, 4(5): 441-447.

压缩感知理论在图像处理领域的应用

基金项目: 

中国科学院二期创新工程基金资助项目(No.C50Top2)

详细信息
  • 中图分类号: TP391.4

Application of compressed sensing theory in image processing

  • 摘要: 针对传统的采样方法得到的图像数据量巨大,给图像信息的后续处理造成极大压力的问题,对压缩感知理论(Compressed Sensing,CS)进行了研究。压缩感知理论使采集很少一部分数据并且从这些少量数据中重构出更大量信息的想法变成可能,突破了奈奎-斯特采样定理的限制。综述了CS理论及关键技术问题,并着重介绍了CS理论在成像系统、图像融合、图像目标识别与跟踪等方面的应用与发展状况。文章指出CS理论开拓了信息处理的新思路,随着该理论的进一步完善,会有更广泛的应用领域。

     

  • [1] CANDÈS E,OMBERG J,TAO T. Robust uncertainty principles:exact signal recognition from highly incomplete frequency information[J]. IEEE Trans. Info. Theory,2006,52(2):489-509. [2] DONOHO D. Compressed sensing[J]. IEEE Trans. Info. Theory,2006,52(4):1289-1306. [3] CANSÈS E,TAO T. Near optimal signal recovery from random projections:universal encoding strategies?[J]. IEEE Trans. Info. Theory,2006,52(12):5406-5425. [4] ELAD M. Optimized projections for compressed sensing[J]. IEEE Trans. Signal Proc.,2007,55(12):5695-5702. [5] APPLEBAUM L,HOWARD S D,SEARLE S,et al.. Chirp sensing codes: deterministic compressed sensing measurements for fast recovery[J]. Appl. Comput, Harmon. Anal.,2009,26:283-290. [6] HERMAN M A,STROHMER T. General deviants: an analysis of perturbations in compressed sensing[J]. IEEE J. Selected Topics in Signal Proc.,2010,4(2):342-349. [7] MA J W. Compressed sensing by inverse scale space and curvelet thresholding[J]. Appl. Math. Comput.,2008,206:980-988. [8] CHRETIEN S. An alternating l1 approach to the compressed sensing problem[J]. IEEE Signal Proc. Lett.,2010,17(2):181-184. [9] CCANDÈS E,WAKIN M,BOYD S. Enhancing sparsity by reweighted l1 minimization[J]. J. Fourier Anal. Appl.,2008,14:877-905. [10] JIN J,GU Y,MEI S. A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework[J]. IEEE J. Selected Topics Signal Proc.,2010,4(2):409-420. [11] BLUMENSATH T,DAVIES M E. Iterative hard thresholding for compressed sensing[J]. Appl. Comput. Harmon. Anal.,2009,27:265-274. [12] RAUHNT H,SCHNASS K,VANDERGHEYNST P,et al.. Compressed sensing and redundant dictionaries[J]. IEEE Trans. Info. Theory,2008,54(5):2210-2219. [13] CANSÈS E J,ELDAR Y C,NEADELL D,et al.. Compressed sensing with coherent and redundant dictionaries[J]. Appl. Comput. Harmon. Anal.,2010,31(1):1-21. [14] DEYRE G. Best basis compressed sensing[J]. IEEE Trans. Signal Proc.,2010,58(5):2613-2622. [15] RAGINSKY M R,WILLETT R M,HARMANY I T,et al.. Compresed sensing performance bounds under poisson noise[J]. IEEE Trans. Signal Proc.,2010,58(8):3990-4002. [16] BARANIUK R G,CEVHER V,DUARTE M F,et al.. Model-based compressive sensing[J]. IEEE Trans. Info. Theory,2010,56(4):1982-2001. [17] HERMAN A,STROHMER T. High-resolution radar via compressed sensing[J]. IEEE Trans. Signal Proc.,2009,57(6):2275-2284. [18] EUDER H G. On compressive sensing applied to radar[J]. Signal Proc.,2010,90:1402-1414. [19] POTTER L C,ERTIN E,PARKER J T,et al.. Sparsity and compressed sensing in radar imaging[J]. Proc. IEEE,2010,98(6):1006-1020. [20] LUSTIG M,DONOHO D L,DANLY J M. Sparse MRI:the application of compressed sensing for rapid MR imaging[J]. Magn. Reson. Med.,2007,58:1182-1195. [21] LUSTIG M,DONOHO D L,SANTOS J M,et al.. Compressed sensing MRI[J]. IEEE Signal Proc Mag.,2008,3:72-82. [22] GAO D H,LIU D H,FENG Y Q,et al.. A robust image transmission scheme for wireless channels based on CS[J]. Lecture notes in Computer Science,2010,6216:334-341. [23] MAJUMDAR A,WARD K. Compressed sensing of color images[J]. Signal Proc.,2010,90:3122-3127. [24] HOLLAND D J,MALIOUTOV D M,BLAKE A,et al.. Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing[J]. J. Magnetic Resonance,2010,203:236-246. [25] MAKALANOBIS A,MUISE R. Object specific image reconstruction using a compressive sensing architecture for application in surveillance systems[J]. IEEE Trans. Aerospace and Electronic Systems,2009,45(3):1167-1180. [26] GIACOBELLO D,CHRISTENSEN M G,MURTHI M,et al.. Retrieving sparse patterns using a compressed sensing framework:applications to speech coding based on sparse linear prediction[J]. IEEE Signal Proc. Lett.,2010,17(1):103-106. [27] MISHALI M,ELDAR Y C. Blind multiband signal reconstruction: compressed sensing for analog signals[J]. IEEE Trans. Signal Proc.,2009,57(3):993-1009. [28] BERGER C R,WANG ZH H,HUANG J ZH,et al.. Application of compressive sensing to sparse channel estimation[J]. IEEE. Commun. Mag.,2010,10:164-174. [29] CAND E J,ROMBERG J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems,2007,23(3):969-985. [30] CAND S E,TAO T. Decoding by linear programming[J]. IEEE Trans. Info.Theory,2005,51(12):4203-4215. [31] HERRMANN F J,HENNENFENT G. Non-parametric seismic data recovery with curvelet frames[J]. Geophysical J. International,2008,173(1):233-248. [32] HERRMANN F J,WANG D L,HENNENFENT G,et al.. Curvelet based seismic data processing:a multiscale and nonlinear approach[J]. Geophysic,2008,73(1):A1. [33] DUARTE M F,DAVENPORT M A,TAKHAR D,et al.. Single-pixel imaging via compressive sampling[J]. IEEE Signal Proc. Mag.,2008,(3):83-91. [34] AKHAR D,LASKA J N,WAKIN M B,et al.. A new compressive imaging camera architecture using optical domain compression[J].SPIE,2006,6065:606509. [35] LUSTIG M,SANTOS J M,DONOHO D L,et al.. Kt SPARSE:High frame rate dynamic MRI exploiting spatiotemporal sparsity. Proceedings of the 14th Annual Meeting of ISMRM,Seattle,Washington,2006:2420 22443. [36] SMITH M I,HEATHER J P. A review of image fusion technology in 2005[J]. SPIE,2005,5782:29-45. [37] 蔡骋,张明,朱俊平. 基于压缩感知理论的杂草种子分类识别[J]. 中国科学:信息科学 ,2010,40(增):160-172. CAI CH,ZHANG M,ZHU J P. Weed seeds classification based on compressive sensing theory[J]. Science China Information Science,2010,40(s):160-172.(in Chinese) [38] LI H X,SHEN CH H. Robust real-time visual tracking with compressed sensing. Proc. of 2010 IEEE 17th International Conference on Image Processing,Hongkong,China,12-15 Sept.2010:45-48. [39] COSSALTER M,TAGLIASACCHI M,VALENZISF G. Privacy-enabled object tracking in video sequences using compressive sensing. Proc. of 2009 IEEE 6th International Conference on Advanced Video and Signal Based Surveillance,Genova,Italy,2-4 Sept,2009:436-441. [40] REDDY D,SANKARANARAYANAN A C,CEVHER V,et al.. Compressed sensing for multi-view tracking and 3-D voxel reconstruction. 2008,15th IEEE International Conference on Image Processing,San Diego,CA,2008:221-224.
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出版历程
  • 收稿日期:  2011-07-21
  • 修回日期:  2011-08-23
  • 刊出日期:  2011-10-25

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