Hierarchical CoSaMP for compressively sampled sparse signals with nested structure

详细信息    查看全文

• 作者：Stefania Colonnese (1)
  Stefano Rinauro (1)
  Katia Mangone (1)
  Mauro Biagi (1)
  Roberto Cusani (1)
  Gaetano Scarano (1)
  
  1. Dipartimento di Ingegneria dell鈥橧nformazione ; Elettronica e delle Telecomunicazioni (DIET) ; Universit脿 di Roma 鈥淟a Sapienza鈥? Via Eudossiana 18 ; Rome ; 00184 ; Italy
  
• 刊名：EURASIP Journal on Advances in Signal Processing
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：2014
• 期：1
• 全文大小：1,977 KB
• 参考文献：1. Needell, D, Tropp, JA (2008) CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmonic Anal 26: pp. 301-321 CrossRef
  2. Baraniuk, RG, Cevher, V, Duarte, MF, Hegde, C (2010) Model-based compressive sensing. IEEE Trans. Inform Theory 56: pp. 1982-2001 CrossRef
  3. Colonnese, S, Cusani, R, Rinauro, S, Scarano, G (2013) Bayesian prior for reconstruction of compressively sampled astronomical images. 4th European Workshop on Visual Information Processing. EUVIP 2013, Paris
  4. Baraniuk, RG, Hegde, C (2011) Sampling and recovery of pulse streams. IEEE Trans. Signal Process 59: pp. 1505-1517 CrossRef
  5. Eldar, Y, Mishali, M (2009) Robust recovery of signals from a union of subspaces. IEEE Trans. Inf. Theory 55: pp. 5302-5316 CrossRef
  6. Colonnese, S, Cusani, R, Rinauro, S, Ruggiero, G, Scarano, G (2013) Efficient compressive sampling of spatially sparse fields in wireless sensor networks. EURASIP J. Adv. Signal Process 136: pp. 1-19
  7. Fazel, F, Fazel, M, Stojanovic, M (2011) Random access compressed sensing for energy-efficient underwater sensor networks. IEEE J. Selected Areas Commun 29: pp. 1660-1670 CrossRef
  8. He, L, Carin, L (2009) Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans. Signal Process 57: pp. 3488-3497 CrossRef
  9. Colonnese, S, Rinauro, S, Mangone, K, Biagi, M, Cusani, R, Scarano, G (2014) Reconstruction of compressively sampled texture images in the graph-based transform domain. IEEE International Conference on Image Processing (ICIP).
  10. Candes, EJ, Tao, T (2005) Decoding by linear programming. IEEE Trans. Inform. Theory 51: pp. 4203-4215 CrossRef
  11. Rudelson, M, Vershynin, R (2008) On sparse reconstruction from Fourier and Gaussian measurements. Comm. Pure Appl. Math 61: pp. 1025-1045 CrossRef
  12. Vershynin, R (2009) On the role of sparsity in Compressed Sensing and Random matrix theory, CAMSAP鈥?9. 3rd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. Aruba, Dutch Antilles
  13. Bah, B, Tanner, J (2010) Improved bounds on restricted isometry constants for Gaussian matrices. J. SIAM J. Matrix Anal. Appl 31: pp. 2882-2898 CrossRef
  14. Mailhe, B, Sturm, B, Plumbley, MD (2013) Behavior of greedy sparse representation algorithms on nested supports. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Vancouver, Canada
  15. Liu, Y, Pados, DA (2013) Decoding of framewise compressed-sensed video via inter-frame total variation minimization. SPIE J. Electron. Imaging Special Issue Compressive Sensing Imaging 22: pp. 1-8
  16. Lee, S, Ortega, A (2012) Adaptive compressive sensing for depthmap compression using graph-based transform. Proceedings of International Conference on Image Processing (ICIP) 2012.
  17. Yap, HL, Eftekhari, A, Wakin, MB, Rozell, CJ (2011) The restricted isometry property for block diagonal matrices. 45th Annual Conference on Information Sciences and Systems (CISS). IEEE, Baltimore
  18. Eftekhari, A, Yap, HL, Rozell, CJ, Wakin, MB (2012) The restricted isometry property for random block diagonal matrices.
  19. Needell, D, Ward, R (2013) Stable image reconstruction using total variation minimization. SIAM J. Imaging Sci 6.2: pp. 1035-1058 CrossRef
  20. Jet Propulsion Laboratoryhttp://ourocean.jpl.nasa.gov. Accessed 26 May 2014
  21. Cheung, G, Kim, W-S, Ortega, A, Ishida, J, Kubota, A (2011) Depth map coding using graph based transform and transform domain sparsification. Proceedings of International Workshop on Multimedia Signal Processing (MMSP). Hangzhou, China
  22. Randen T: Brodatz textures. http://www.ux.uis.no/~tranden/brodatz.html. Accessed 26 May 2014
  23. Baraniuk RG, Cevher V, Duarte MF, Hegde C: Model-based Compressive Sensing Toolbox v1.1. http://dsp.rice.edu/software/model-based-compressive-sensing-toolbox. Accessed 26 May 2014
  24. Campisi, P, Neri, A, Scarano, G (2000) Reduced complexity modeling and reproduction of colored textures. IEEE Trans. Image Process 9: pp. 510-518 CrossRef
  25. Campisi, P, Colonnese, S, Panci, G, Scarano, G (2006) Reduced complexity rotation invariant texture classification using a blind deconvolution approach. IEEE Trans. Pattern Anal. Mach. Intell 28: pp. 145-149 CrossRef
  26. Carin L, Ji S, Xue Y: Bayesian Compressive Sensing code. http://people.ee.duke.edu/~lcarin/BCS.html. Accessed 25 May 2014
  
• 刊物主题：Signal, Image and Speech Processing;
• 出版者：Springer International Publishing
• ISSN：1687-6180

文摘

This paper presents a novel procedure, named Hierarchical Compressive Sampling Matching Pursuit (CoSaMP), for reconstruction of compressively sampled sparse signals whose coefficients are organized according to a nested structure. The Hierarchical CoSaMP is inspired by the CoSaMP algorithm, and it is based on a suitable hierarchical extension of the support over which the compressively sampled signal is reconstructed. We analytically demonstrate the convergence of the Hierarchical CoSaMP and show by numerical simulations that the Hierarchical CoSaMP outperforms state-of-the-art algorithms in terms of accuracy for a given number of measurements at a restrained computational complexity.