Have a personal or library account? Click to login

Wireless Multimedia Sensor Network Image De-Noising via a Detail-Preserving Sparse Model

Cybernetics and Information Technologies
Volume 15 (2015): Issue 6 (December 2015)
By:
Zhi Cui and  Xian-pu Cui  
Open Access
|Dec 2015

References

  1. 1. Akyildiz, I., T. Melodia, K. Chowdhury. A Survey on Wireless Multimedia Sensor Networks. – Computer Networks, 2007, No 51, pp. 921-960.10.1016/j.comnet.2006.10.002
    Search in Google Scholar
  2. 2. Pinar, S., I. Kerem, B. Sebnem, E. Harmanci. Image Quality Estimation in Wireless Multimedia Sensor Networks: An Experimental Study. – BROADNETS, LNICST, Vol. 66, 2012, No 2, pp. 226-241.10.1007/978-3-642-30376-0_15
    Search in Google Scholar
  3. 3. Zhang, Q. Y., H. P. Huang. Image Compression Algorithm Using Probability Density Function Estimation in Wireless Multimedia Sensor Network. – Journal of Computational Information Systems, Vol. 8, 2012, No 17, pp. 7223-7229.
    Search in Google Scholar
  4. 4. Chia, W. C., L. M. Ang, K. P. Seng. Multiview Image Compression for Wireless Multimedia Sensor Network Using Image Stitching and SPIHT Coding With EZW Tree Structure. – In: Proc. of International Conference on Intelligent Human-Machine Systems and Cybernetics, 2009, pp. 298-301.10.1109/IHMSC.2009.198
    Search in Google Scholar
  5. 5. Rein, S., M. Reisslein. Performance Evaluation of the Fractional Wavelet Filter: A Low-Memory Image Wavelet Transform for Multimedia Sensor Networks. – Ad Hoc Networks, Vol. 9, 2011, No 4, pp. 482-496.10.1016/j.adhoc.2010.08.004
    Search in Google Scholar
  6. 6. Wang, W., D. M. Peng, H. G. Wang, H. Sharif, H. H. Chen. Energy-Constrained Distortion Reduction Optimization for Wavelet-Based Coded Image Transmission in Wireless Sensor Networks. – IEEE Transactions on Multimedia, Vol. 10, 2008, No 6, pp. 1169-1180.10.1109/TMM.2008.2001354
    Search in Google Scholar
  7. 7. Costa, D. G., L. A. Guedes, F. Vasques, P. Portugal. Delay-Aware DWT-Based Image Transmission in Wireless Visual Sensor Networks. – In: Proc. of 19th Brazilian Symposium on Multimedia and the Web, 2013, pp. 157-164.10.1145/2526188.2526200
    Search in Google Scholar
  8. 8. Yin, M., W. Liu, J. Shui, J. M. Wu. Quaternion Wavelet Analysis and Application in Image Denoising.– Mathematical Problems in Engineering, 2012, pp. 1-21.10.1155/2012/493976
    Search in Google Scholar
  9. 9. Yan, F. X., S. L. Peng, L. Z. Cheng. Dual-Tree Complex Wavelet Hidden Markov Tree Model for Image Denoising. – Electronics Letters, Vol. 43, 2007, No 18, pp. 973-975.10.1049/el:20071258
    Search in Google Scholar
  10. 10. Lu, M. Z., Z. Q. Liu, M. X. Shen, L. S. Liu, X. J. Yang, B. Zhou. Image Wavelet Transform on Low Memory Sensor Nodes of WMSN. – Transactions of the Chinese Society for Agricultural Machinery, Vol. 45, 2014, No 4, pp. 289-293.
    Search in Google Scholar
  11. 11. Zhang, Q., H. P. Huang. Image Compression Algorithm Using Probability Density Function Estimation in Wireless Multimedia Sensor Network. – Journal of Computational Information Systems, Vol. 8, 2012, No 17, pp. 7223-7229.
    Search in Google Scholar
  12. 12. Xiang, Q. M., J. G. Zhang, X. Luo, Y. Y. Cheng, C. Wang. Image Compression for Wildlife Monitoring Based on Wireless Multimedia Sensor Network. – Journal of Computational Information Systems, Vol. 8, 2012, No 17, pp. 7223-7229.
    Search in Google Scholar
  13. 13. Wang, P., R. Dai, I. F. Akyildiz. A Spatial Correlation-Based Image Compression Framework for Wireless Multimedia Sensor Networks. – IEEE Transactions on Multimedia, Vol. 13, 2011, No 2, pp. 388-401.10.1109/TMM.2010.2100374
    Search in Google Scholar
  14. 14. Yu, N. N., T. S. Qiu, F. Y. Ren. Denoising for Multiple Image Copies through Joint Sparse Representation. – Journal of Mathematical Imaging and Vision, Vol. 45, 2013, No 1, pp. 46-54.10.1007/s10851-012-0343-1
    Search in Google Scholar
  15. 15. Kuang, Y., L. Zhang, Z. Yi. An Adaptive Rank-Sparsity K-SVD Algorithm for Image Sequence Denoising. – Pattern Recognition Letters, Vol. 45, 2014, No 1, pp. 46-54.10.1016/j.patrec.2014.03.003
    Search in Google Scholar
  16. 16. Elad, M., M. Aharon. Image Denoising via Learned Dictionaries and Sparse Representation. – IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 895-900.
    Search in Google Scholar
  17. 17. Aharon, M., M. Elad, Bruckstein. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. – IEEE Transactions on Image Processing, Vol. 54, 2006, No 11, pp. 4311-4322.10.1109/TSP.2006.881199
    Search in Google Scholar
  18. 18. Li, S. T., L.Y. Fang, H. T. Yin. An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising. – IEEE Transactions on Biomedical Engineering, Vol. 59, 2012, No 2, pp. 417-427.10.1109/TBME.2011.217393522049358
    Search in Google Scholar
  19. 19. Fang, L. Y., S. T. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu. Sparsity Based Denoising of Spectral Domain Optical Coherence Tomography Images. – Biomedical Optics Express, Vol. 3, 2012, No 5, pp. 927-942.10.1364/BOE.3.000927334219822567586
    Search in Google Scholar
  20. 20. Fang, L. Y., S. T. Li, R. McNabb, Q. Nie, A. Kuo, C. Toth, J. A. Izatt, S. Farsiu. Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation. – IEEE Transactions on Medical Imaging, Vol. 32, 2013, No 11, pp. 2034-2049.10.1109/TMI.2013.2271904400055923846467
    Search in Google Scholar
  21. 21. Zhang, F., K. Xie. A Novel Image Denoising Method Based on DCT Basis and Sparse Representation. – In: Proc. of Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, 2011, pp. 26-30.10.1109/CSQRWC.2011.6037203
    Search in Google Scholar
  22. 22. Zhang, Q., Y. Fu, L. C. Li, J. Y. Yang. A Millimeter-Wave Image Denoising Method Based On Adaptive Sparse Representation. – In: Proc. of International Conference on Computational Problem-Solving, 2011, pp. 652-655.10.1109/ICCPS.2011.6089764
    Search in Google Scholar
  23. 23. Zhou, Z., L. M. Luo. Research on Image Denoising Algorithm Based on Adaptive Overcomplete Sparse Representation Theories. – Journal of Convergence and Information Technology, Vol. 7, 2012, No 16, pp. 315-321.10.4156/jcit.vol7.issue16.38
    Search in Google Scholar
  24. 24. Zhou, W., A. C. Bovik. Mean Square Error: Love It or Leave It? A New Look at Signal Fidelity Measures. – IEEE Signal Processing Magazine, Vol. 26, 2009, No 1, pp. 98-117.10.1109/MSP.2008.930649
    Search in Google Scholar
  25. 25. Li, S. T., H. T. Yin, L. Y. Fang. Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, 2013, No 9, pp. 4779-4789.10.1109/TGRS.2012.2230332
    Search in Google Scholar
  26. 26. Chen, S. S., D. L. Donoho, M. A. Saunders. Atomic Decomposition by Basis Pursuit. – Siam Review, Vol. 43, 2001, No 1, pp. 129-159.10.1137/S003614450037906X
    Search in Google Scholar
  27. 27. Mallat, S. G., Z. Zhang. Matching Pursuits with Time-Frequency Dictionaries. – IEEE Transactions on Signal Processing, Vol. 41, 1993, No 12, pp. 3397-3415.10.1109/78.258082
    Search in Google Scholar
  28. 28. Chen, Z., Y. Y. Chung, H. Chen. Sure-Let Based Sparse Representation Image Denoising. – ICIC Express Letters, Part B: Applications, Vol. 5, 2014, No 3, pp. 739-744.
    Search in Google Scholar
  29. 29. Wang, S. Z. Sparse Matrix Method Image Denoising Based on SVD. – International Journal of Multimedia and Ubiquitous Engineering, Vol. 9, 2014, No 7, pp. 227-236.10.14257/ijmue.2014.9.7.19
    Search in Google Scholar
  30. 30. Hu, J. R., Y. F. Pu, Y. Zhang, Y. Liu, J. L. Zhou. A Novel Nonlocal Means Denoising Method Using the DCT. – In: Proc. of International Conference on Image Processing, Computer Vision, and Pattern Recognition, 2011, pp. 865-869.
    Search in Google Scholar
  31. 31. Wang, X. L., X. Y. Wang, H. Cao. Image Denoising Based on a New Wavelet Statistical Model. – In: Proc. of International Conference on Intelligent Systems Design and Applications, 2006, pp. 342-346.10.1109/ISDA.2006.253859
    Search in Google Scholar
DOI: https://doi.org/10.1515/cait-2015-0067 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Journal RSS Feed
Language: English
Page range: 57 - 69
Published on: Dec 30, 2015
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Structural similarity,
residual ratio,
wireless multimedia sensor network,
sparse representation,
de-noising
Related subjects:
Computer sciences,
Information technology

© 2015 Zhi Cui, Xian-pu Cui, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous articleVolume 15 (2015): Issue 6 (December 2015)Next article